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X-ray luminescence computed tomography (XLCT) is a hybrid molecular imaging modality having the high spatial resolution of x-ray imaging and high measurement sensitivity of optical imaging. Narrow x-ray beam based XLCT imaging has shown promise for high spatial resolution imaging of luminescent targets in deep tissues, but the slow acquisition speed limits its applications. In this work, we have introduced a superfast XLCT scan scheme based on the photon counter detector and a fly-scanning method. The new scan scheme is compared with three other scan methods. We have also designed and built a single-pixel x-ray detector to detect object boundaries automatically. With the detector, we can perform the parallel beam CT imaging with the XLCT imaging simultaneously. We have built the prototype XLCT imaging system to verify the proposed scan scheme. A phantom embedded with a set of four side-by-side cylindrical targets was scanned. With the proposed superfast scan scheme, we have achieved 43 seconds per transverse scan, which is 28.6 times faster than before with slightly better XLCT image quality. The superfast scan allows us to perform 3D pencil beam XLCT imaging in the future.

Histopathology as a diagnostic mainstay for tissue evaluation is strictly a 2D technology. Combining and supplementing this technology with 3D imaging has been proposed as one future avenue towards refining comprehensive tissue analysis. To this end, we have developed a laboratory-based X-ray method allowing for the investigation of tissue samples in three dimensions with isotropic volume information. To assess the potential of our method for micro-morphology evaluation, we selected several kidney regions from three patients with cystic kidney disease, obstructive nephropathy and diabetic glomerulopathy. Tissue specimens were processed using our in-house-developed X-ray eosin stain and investigated with a commercial microCT and our in-house-built NanoCT. The microCT system provided overview scans with voxel sizes of $14.8~\mu \text{m}$ and the NanoCT was employed for higher resolutions including voxel sizes from $1.4~\mu \text{m}$ to 210 nm. We present a methodology allowing for a precise micro-morphologic investigation in three dimensions which is compatible with conventional histology. Advantages of our methodology are its versatility with respect to multi-scale investigations, being laboratory-based, allowing for non-destructive imaging and providing isotropic volume information. We believe, that after future developmental work this method might contribute to advanced multi-modal tissue diagnostics. [ABSTRACT FROM AUTHOR]

To reduce the potential risk of radiation to the patient, low-dose computed tomography (LDCT) has been widely adopted in clinical practice for reconstructing cross-sectional images using sinograms with reduced x-ray flux. The LDCT image quality is often degraded by different levels of noise depending on the low-dose protocols. The image quality will be further degraded when the patient has metallic implants, where the image suffers from additional streak artifacts along with further amplified noise levels, thus affecting the medical diagnosis and other CT-related applications. Previous studies mainly focused either on denoising LDCT without considering metallic implants or full-dose CT metal artifact reduction (MAR). Directly applying previous LDCT or MAR approaches to the issue of simultaneous metal artifact reduction and low-dose CT (MARLD) may yield sub-optimal reconstruction results. In this work, we develop a dual-domain under-to-fully-complete progressive restoration network, called DuDoUFNet, for MARLD. Our DuDoUFNet aims to reconstruct images with substantially reduced noise and artifact by progressive sinogram to image domain restoration with a two-stage progressive restoration network design. Our experimental results demonstrate that our method can provide high-quality reconstruction, superior to previous LDCT and MAR methods under various low-dose and metal settings. [ABSTRACT FROM AUTHOR]

We demonstrate that Domain Invariant Feature Learning (DIFL) can improve the out-of-domain generalizability of a deep learning Tuberculosis (TB) screening algorithm. It is well known that state of the art deep learning algorithms often have difficulty generalizing to unseen data distributions due to “domain shift.” In the context of medical imaging, this could lead to unintended biases such as the inability to generalize from one patient population to another. We analyze the performance of a ResNet-50 classifier for the purposes of TB screening using the four most popular public datasets with geographically diverse sources of imagery. We show that without domain adaptation, ResNet-50 has difficulty in generalizing between imaging distributions from a number of public TB screening datasets with imagery from geographically distributed regions. However, with the incorporation of DIFL, the out-of-domain performance is greatly enhanced. Analysis criteria includes a comparison of accuracy, sensitivity, specificity and AUC over both the baseline, as well as the DIFL enhanced algorithms. We conclude that DIFL improves generalizability of TB screening while maintaining acceptable accuracy over the source domain imagery when applied across a variety of public datasets.

During clinical practice, radiologists often use attributes, e.g., morphological and appearance characteristics of a lesion, to aid disease diagnosis. Effectively modeling attributes as well as all relationships involving attributes could boost the generalization ability and verifiability of medical image diagnosis algorithms. In this paper, we introduce a hybrid neuro-probabilistic reasoning algorithm for verifiable attribute-based medical image diagnosis. There are two parallel branches in our hybrid algorithm, a Bayesian network branch performing probabilistic causal relationship reasoning and a graph convolutional network branch performing more generic relational modeling and reasoning using a feature representation. Tight coupling between these two branches is achieved via a cross-network attention mechanism and the fusion of their classification results. We have successfully applied our hybrid reasoning algorithm to two challenging medical image diagnosis tasks. On the LIDC-IDRI benchmark dataset for benign-malignant classification of pulmonary nodules in CT images, our method achieves a new state-of-the-art accuracy of 95.36% and an AUC of 96.54%. Our method also achieves a 3.24% accuracy improvement on an in-house chest X-ray image dataset for tuberculosis diagnosis. Our ablation study indicates that our hybrid algorithm achieves a much better generalization performance than a pure neural network architecture under very limited training data. [ABSTRACT FROM AUTHOR]

Tensor fields are useful for modeling the structure of biological tissues. The challenge to measure tensor fields involves acquiring sufficient data of scalar measurements that are physically achievable and reconstructing tensors from as few projections as possible for efficient applications in medical imaging. In this paper, we present a filtered back-projection algorithm for the reconstruction of a symmetric second-rank tensor field from directional X-ray projections about three axes. The tensor field is decomposed into a solenoidal and irrotational component, each of three unknowns. Using the Fourier projection theorem, a filtered back-projection algorithm is derived to reconstruct the solenoidal and irrotational components from projections acquired around three axes. A simple illustrative phantom consisting of two spherical shells and a 3D digital cardiac diffusion image obtained from diffusion tensor MRI of an excised human heart are used to simulate directional X-ray projections. The simulations validate the mathematical derivations and demonstrate reasonable noise properties of the algorithm. The decomposition of the tensor field into solenoidal and irrotational components provides insight into the development of algorithms for reconstructing tensor fields with sufficient samples in terms of the type of directional projections and the necessary orbits for the acquisition of the projections of the tensor field. [ABSTRACT FROM AUTHOR]

Recent advances in deep learning led to several algorithms for the accurate diagnosis of pneumonia from chest X-rays. However, these models require large training medical datasets, which are sparse, isolated, and generally private. Furthermore, these models in medical imaging are known to over-fit to a particular data domain source, i.e., these algorithms do not conserve the same accuracy when tested on a dataset from another medical center, mainly due to image distribution discrepancies. In this work, a domain adaptation and classification technique is proposed to overcome the over-fit challenges on a small dataset. This method uses a private-small dataset (target domain), a public-large labeled dataset from another medical center (source domain), and consists of three steps. First, it performs a data selection of the source domain’s most representative images based on similarity constraints through principal component analysis subspaces. Second, the selected samples from the source domain are fit to the target distribution through an image to image translation based on a cycle-generative adversarial network. Finally, the target train dataset and the adapted images from the source dataset are used within a convolutional neural network to explore different settings to adjust the layers and perform the classification of the target test dataset. It is shown that fine-tuning a few specific layers together with the selected-adapted images increases the sorting accuracy while reducing the trainable parameters. The proposed approach achieved a notable increase in the target dataset’s overall classification accuracy, reaching up to 97.78 % compared to 90.03 % by standard transfer learning. [ABSTRACT FROM AUTHOR]

Knee osteoarthritis (KOA) as a disabling joint disease has doubled in prevalence since the mid-20th century. Early diagnosis for the longitudinal KOA grades has been increasingly important for effective monitoring and intervention. Although recent studies have achieved promising performance for baseline KOA grading, longitudinal KOA grading has been seldom studied and the KOA domain knowledge has not been well explored yet. In this paper, a novel deep learning architecture, namely adversarial evolving neural network (A-ENN), is proposed for longitudinal grading of KOA severity. As the disease progresses from mild to severe level, ENN involves the progression patterns for accurately characterizing the disease by comparing an input image it to the template images of different KL grades using convolution and deconvolution computations. In addition, an adversarial training scheme with a discriminator is developed to obtain the evolution traces. Thus, the evolution traces as fine-grained domain knowledge are further fused with the general convolutional image representations for longitudinal grading. Note that ENN can be applied to other learning tasks together with existing deep architectures, in which the responses characterize progressive representations. Comprehensive experiments on the Osteoarthritis Initiative (OAI) dataset were conducted to evaluate the proposed method. An overall accuracy was achieved as 62.7%, with the baseline, 12-month, 24-month, 36-month, and 48-month accuracy as 64.6%, 63.9%, 63.2%, 61.8% and 60.2%, respectively. [ABSTRACT FROM AUTHOR]

Breast microcalcifications are an important primary radiological indicator of breast cancer. However, microcalcification classification and diagnosis may be still challenging for radiologists due to limitations of the standard 2D mammography technique, including spatial and contrast resolution. In this study, we propose an approach to improve the detection of microcalcifications in propagation-based phase-contrast X-ray computed tomography of breast tissues. Five fresh mastectomies containing microcalcifications were scanned at different X-ray energies and radiation doses using synchrotron radiation. Both bright-field (i.e. conventional phase-retrieved images) and dark-field images were extracted from the same data sets using different image processing methods. A quantitative analysis was performed in terms of visibility and contrast-to-noise ratio of microcalcifications. The results show that while the signal-to-noise and the contrast-to-noise ratios are lower, the visibility of the microcalcifications is more than two times higher in the dark-field images compared to the bright-field images. Dark-field images have also provided more accurate information about the size and shape of the microcalcifications. [ABSTRACT FROM AUTHOR]

X-ray photon-counting detectors capable of resolving the energies of single X-ray photons are critical in medical imaging, and a high count rate is essential for photon-counting detectors. Here, we report the performance of the perovskite CsPbBr3 single-crystal semiconductor detector for X-ray photon counting. The CsPbBr3 detector noise floor, energy response linearity, energy resolution, count rate, and polarization were evaluated. By fine-tuning the detector working conditions, our CsPbBr3 detector with a planar electrode can count an incident X-ray photon rate of ~0.099 and ~0.336 Mcps/pixel at 10% and 30% deadtime loss, respectively, with corresponding energy resolutions of ~18% at 59.5 keV and ~12% at 122 keV at same pulse processing conditions. We also demonstrated that our CsPbBr3 detectors show negligible polarization under an X-ray flux of ~0.45 M photons/s/mm2 for the typical timescale of multiple clinical X-ray scans, such as 1 s– 100 s. Our evaluation demonstrates the high potential of CsPbBr3 detectors for X-ray photon-counting applications in medical and industrial diagnostics with a lower cost than the current state-of-the-art. [ABSTRACT FROM AUTHOR]

Screening cluttered and occluded contraband items from baggage X-ray scans is a cumbersome task even for the expert security staff. This article presents a novel strategy that extends a conventional encoder-decoder architecture to perform instance-aware segmentation and extract merged instances of contraband items without using any additional subnetwork or an object detector. The encoder-decoder network first performs conventional semantic segmentation and retrieves cluttered baggage items. The model then incrementally evolves during training to recognize individual instances using significantly reduced training batches. To avoid catastrophic forgetting, a novel objective function minimizes the network loss in each iteration by retaining the previously acquired knowledge while learning new class representations and resolving their complex structural interdependencies through Bayesian inference. A thorough evaluation of our framework on two publicly available X-ray datasets shows that it outperforms state-of-the-art methods, especially within the challenging cluttered scenarios, while achieving an optimal tradeoff between detection accuracy and efficiency. [ABSTRACT FROM AUTHOR]

The excellent mechanical properties of Polydimethylsilane (PDMS) has been widely used in hybrid flexible photodetectors, but there is hardly any research on it as the active layer of the direct X-ray detectors. In this letter, the low detection and high flexibility X-ray detector was fabricated based on pure PDMS. The metal/polymer/metal (MPM) vertical structure detector has a detection limit of 49.69 nGy $_{air}\text{s}$ −1. Carrier migration occurs in PDMS under X-ray irradiation with the sensitivity of $57.7~\mu \text{C}$ Gy $_{air}^{-1}$ cm−2 at the electric field of 2 V/ $\mu \text{m}$ and the dose rate of $23.9~\mu $ Gy $_{air}$ s−1. The device still maintains a good performance after bending tests (different bending radius and 10000 cycles). This work provides a new possibility for the next development of direct X-ray detectors. [ABSTRACT FROM AUTHOR]

X-ray phase contrast imaging (XPCI) is an emerging imaging modality for industrial inspections and biomedical applications. A polycapillary semilens is utilized in the grating-based XPCI to reduce the imaging time or improve the image quality of the reconstructed images in this work. The proposed system is proved to be effective for improving the contrast-to-noise ratio (CNR) of the reconstructed images compared with using an aperture diaphragm in the system under the same exposure time. The polycapillary semilens concentrates the X-rays on the region of interest (ROI) of the sample, which reduces the unnecessary radiation in other areas. [ABSTRACT FROM AUTHOR]

Metal halides have been demonstrated to be promising candidates for X-ray detectors. However, its large leakage current caused by the severe ion migration and intrinsic defect substantially degrades device performance. In this work, an asymmetric BA2CsPb2I7-CsPbI3 planar heterojunction X-ray detector is designed to suppress the leakage current. Both experimental and theoretical calculations demonstrate that: 1) enlarged ion migration energy (0.80 eV) and conductivity mutation at the interface ensure stable baseline and suppress leakage current; 2) CsPbI3 ensures effective absorption of X-ray, while BA2CsPb2I7 induces larger bulk resistance, which is expected to achieve higher photocurrent and lower dark current. Finally, the obtained X-ray detector exhibits negligible baseline drift, faster response, and smaller leakage current compared to the CsPbI3 counterparts. Specifically, the device exhibits a detection limit of $0.092 ~\mu $ Gy $_{air}$ /s, and the CsPbI3-based device was $2.63 ~\mu $ Gy $_{air}$ /s. [ABSTRACT FROM AUTHOR]

In tire defect detection, an X-ray image sensor is utilized to collect samples, upon which automatic defect detection methods are constructed. The performance of existing defect detection methods is reduced significantly when working condition shifts. Fortunately, domain adaptation can address this domain shift problem effectively. However, previous methods only align the marginal distribution or conditional distribution, while ignoring the unified adaptation of the two distributions, and may result in an unsatisfactory transfer effect. To this end, this article proposes a dual-domain adaptation model, which harnesses the adaptation of global marginal distribution and fine-grained conditional distribution simultaneously. Specifically, to learn the feature with the consideration of intraclass compact and interclass discriminative, the relative importance of marginal distribution and conditional distribution is measured by a dynamic adaptive factor. In addition, a multihead self-attention (MSA) mechanism is designed in the feature extractor for global context modeling. To evaluate the effectiveness of the proposed method, the tire defect detection task on five kinds of familiar defects, namely slack, bend, overlap, impurity, and bubble, was constructed. Compared with other existing domain adaptation models, the proposed model can achieve 96.39% detect detection accuracy. The experimental results demonstrate the effectiveness of the proposed method in tire defect detection under the domain shift. [ABSTRACT FROM AUTHOR]

Locating diseases in chest X-ray images with few careful annotations saves large human effort. Recent works approached this task with innovative weakly-supervised algorithms such as multi-instance learning (MIL) and class activation maps (CAM), however, these methods often yield inaccurate or incomplete regions. One of the reasons is the neglection of the pathological implications hidden in the relationship across anatomical regions within each image and the relationship across images. In this paper, we argue that the cross-region and cross-image relationship, as contextual and compensating information, is vital to obtain more consistent and integral regions. To model the relationship, we propose the Graph Regularized Embedding Network (GREN), which leverages the intra-image and inter-image information to locate diseases on chest X-ray images. GREN uses a pre-trained U-Net to segment the lung lobes, and then models the intra-image relationship between the lung lobes using an intra-image graph to compare different regions. Meanwhile, the relationship between in-batch images is modeled by an inter-image graph to compare multiple images. This process mimics the training and decision-making process of a radiologist: comparing multiple regions and images for diagnosis. In order for the deep embedding layers of the neural network to retain structural information (important in the localization task), we use the Hash coding and Hamming distance to compute the graphs, which are used as regularizers to facilitate training. By means of this, our approach achieves the state-of-the-art result on NIH chest X-ray dataset for weakly-supervised disease localization. Our codes are accessible online. [ABSTRACT FROM AUTHOR]

Lead-based organic–inorganic hybrid perovskite single crystals (OIHP SCs) offer promising opportunity for X-ray detection and imaging owing to their high X-ray attenuation coefficient, excellent optoelectronic properties, and simple fabrication processes. Nevertheless, it remains a key challenge to reduce its dark current and meanwhile maintain a high sensitivity. Herein, we developed an on-substrate method for growing methylamine lead bromide (MAPbBr3) SCs on aluminum oxide (Al2O3) layer coated indium tin oxide (ITO) glass. The photodiode based on this approach exhibits an ultralow-dark-current density of 1 nA cm−2 at an operative voltage of −5 V, a signal-to-noise ratio (SNR) more than 80 dB, and meanwhile retaining a high X-ray sensitivity of 2.02 $\times 10^{{3}}\,\, \mu \text{C}\cdot $ ${\mathrm {Gy}}_{\text {air}}^{-1} \cdot $ cm−2. Our further investigation reveals that the dark current was reduced due to two beneficial effects of the Al2O3 layer in tandem, including the interfacial defect passivation and blocking of hole injection by increased interfacial energy barrier. Our approach sheds light on advancing the fabrication of high-performance OIHP SCs-based X-ray sensors that can be potentially integrated with large-area readout panel at low temperature. [ABSTRACT FROM AUTHOR]

In many real world medical image classification settings, access to samples of all disease classes is not feasible, affecting the robustness of a system expected to have high performance in analyzing novel test data. This is a case of generalized zero shot learning (GZSL) aiming to recognize seen and unseen classes. We propose a GZSL method that uses self supervised learning (SSL) for: 1) selecting representative vectors of disease classes; and 2) synthesizing features of unseen classes. We also propose a novel approach to generate GradCAM saliency maps that highlight diseased regions with greater accuracy. We exploit information from the novel saliency maps to improve the clustering process by: 1) Enforcing the saliency maps of different classes to be different; and 2) Ensuring that clusters in the space of image and saliency features should yield class centroids having similar semantic information. This ensures the anchor vectors are representative of each class. Different from previous approaches, our proposed approach does not require class attribute vectors which are essential part of GZSL methods for natural images but are not available for medical images. Using a simple architecture the proposed method outperforms state of the art SSL based GZSL performance for natural images as well as multiple types of medical images. We also conduct many ablation studies to investigate the influence of different loss terms in our method. [ABSTRACT FROM AUTHOR]

Osteoporosis is still a worldwide problem, particularly due to associated fragility fractures. Patients at risk of fracture are currently detected using the X-Ray gold standard dual-energy X-ray absorptiometry (DXA), based on a calibrated 2-D image. Different alternatives, such as 3-D X-rays, magnetic resonance imaging (MRI) or ultrasound, have been proposed, the latter having advantages of being portable and sensitive to mechanical and geometrical properties. Bidirectional axial transmission (BDAT) has been used to classify between patients with or without nontraumatic fractures using “classical” ultrasonic parameters, such as velocities, as well as cortical thickness and porosity, obtained from an inverse problems. Recently, complementary parameters acquired with structural and textural analysis of guided wave spectrum images (GWSIs) have been introduced. These parameters are not limited by solution ambiguities, as for inverse problem. The aim of the study is to improve the patient classification using a feature selection strategy for all available ultrasound features completed by clinical parameters. To this end, three classical feature ranking methods were considered: analysis of variance (ANOVA), recursive feature elimination (RFE), and extreme gradient boosting importance feature (XGBI). In order to evaluate the performance of the feature selection techniques, three classical classification methods were used: logistic regression (LR), support vector machine (SVM), and extreme gradient boosting (XGB). The database was obtained from a previous clinical study [Minonzio et al., 2019]. Results indicate that the best accuracy of 71 [66–76]% was achieved by using RFE and SVM with 22 (out of 43) ultrasonic and clinical features. This value outperformed the accuracy of 68 [64–73]% reached with 2 (out of 6) DXA and clinical features. These values open promising perspectives toward improved and generalizable classification of patients at risk of fracture. [ABSTRACT FROM AUTHOR]

For the 2020 Student Cluster Competition, we reproduced results from “MemXCT: Memory-Centric X-ray CT Reconstruction with Massive Parallelization” (Hidayetoğlu et al.). Reproducibility is of critical importance to the scientific community, not just to verify correctness of results but also to see how easily others can understand and work with the given methods. MemXCT is an approach for image reconstruction in X-ray ptychography, which has a broad range of applications in materials science. MemXCT is not the only X-ray tomography algorithm, though; as opposed to compute-centric algorithms, it is designed to scale better by optimizing for memory bandwidth and memory latency. MemXCT also applies several key optimizations in order to ease memory pressure. In this article, we test the performance and strong scaling of MemXCT on 1 to 256 AMD CPU cores (1-4 nodes) and 1-16 Nvidia V100 GPUs (1-4 nodes). We confirm the impact of MemXCT’s optimizations. Still, we find that the performance of some important loops in the MemXCT kernel is much lower on the AMD processors (with AVX2) of our CPU nodes compared to the Intel CPUs (with AVX-512) used in the original article. We also confirm MemXCT performance on Tesla V100 GPUs, as reported in the article. [ABSTRACT FROM AUTHOR]

Hidayetoğlu et al. propose a novel memory-centric algorithm to reconstruct X-ray CT images in the SC19 article entitled “MemXCT: Memory-Centric X-ray CT Reconstruction with Massive Parallelization”. They formulate the reconstruction with several SpMVs, and propose two memory-centric optimizations to improve cache locality for better memory bandwidth utilization, i.e., a two-level pseudo-Hilbert ordering and a multi-stage input buffering. In this article, we present our results on reproducing that article to show its effectiveness and generality, as part of the SC20 Student Cluster Competition Reproducibility Challenge. We reproduce the execution time and memory bandwidth tests in that article on various architectures, including Intel CPUs, AMD CPUs, and NVIDIA GPUs. We further analyze the bottleneck on different architectures by comparing the achieved memory bandwidth with the peak bandwidth on those architectures. We then reproduce the strong scaling test on CPU and GPU clusters with different scales, and use the proposed algorithm to reconstruct three new X-ray computed tomograms. [ABSTRACT FROM AUTHOR]

This work extends our previous research entitled “MemXCT: Memory-centric X-ray CT Reconstruction with Massive Parallelization” that was originally published at SC19 conference (Hidayetoğlu et al., 2019) with reproducibility of the computational imaging performance. X-ray computed tomography (XCT) is regularly used at synchrotron light sources to study the internal morphology of materials at high resolution. However, experimental constraints, such as radiation sensitivity, can result in noisy or undersampled measurements. Further, depending on the resolution, sample size and data acquisition rates, the resulting noisy dataset can be in the order of terabytes. Advanced iterative reconstruction techniques can produce high-quality images from noisy measurements, but their computational requirements have made their use an exception rather than the rule. We propose a novel memory-centric approach that avoids redundant computations at the expense of additional memory complexity. We develop a memory-centric iterative reconstruction system, MemXCT, that uses an optimized SpMV implementation with two-level pseudo-Hilbert ordering and multi-stage input buffering. We evaluate MemXCT on various supercomputer architectures involving KNL and GPU. MemXCT can reconstruct a large (11K×11K) mouse brain tomogram in 10 seconds using 4096 KNL nodes (256K cores). The results presented in our original article at the SC19 were based on large-scale supercomputing resources. The MemXCT application was selected for the Student Cluster Competition (SCC) Reproducibility Challenge and evaluated on a variety of cloud computing resources by universities around the world in the SC20 conference. We summarize the results of the top-ranked SCC Reproducibility Challenge teams and identify the most pertinent measures for ensuring the reproducibility of our experiments in this article. [ABSTRACT FROM AUTHOR]

A robust ohmic contact process with ultralow contact resistivity on p-GaN/AlGaN/GaN is demonstrated. An in-situ removal of GaOX interfacial layer after contact metal deposition is developed. Using the novel Mg/Pt/Au stack as the contact metal, a stable ohmic contact is obtained after 450°C/300 s annealing with ohmic contact resistivity of $12 \Omega \cdot {\mathrm {mm}}$ ($1.8\times 10^{-5}\, \Omega \cdot {\mathrm {cm}}^{2}$). A new ohmic contact formation mechanism on p-GaN/AlGaN/GaN is also proposed. [ABSTRACT FROM AUTHOR]

Around 60-80% of radiological errors are attributed to overlooked abnormalities, the rate of which increases at the end of work shifts. In this study, we run an experiment to investigate if artificial intelligence (AI) can assist in detecting radiologists’ gaze patterns that correlate with fatigue. A retrospective database of lung X-ray images with the reference diagnoses was used. The X-ray images were acquired from 400 subjects with a mean age of 49 ± 17, and 61% men. Four practicing radiologists read these images while their eye movements were recorded. The radiologists passed a series of concentration tests at prearranged breaks of the experiment. A U-Net neural network was adapted to annotate lung anatomy on X-rays and calculate coverage and information gain features from the radiologists’ eye movements over lung fields. The lung coverage, information gain, and eye tracker-based features were compared with the cumulative work done (CDW) label for each radiologist. The gaze-traveled distance, X-ray coverage, and lung coverage statistically significantly (p < 0.01) deteriorated with cumulative work done (CWD) for three out of four radiologists. The reading time and information gain over lungs statistically significantly deteriorated for all four radiologists. We discovered a novel AI-based metric blending reading time, speed, and organ coverage, which can be used to predict changes in the fatigue-related image reading patterns. [ABSTRACT FROM AUTHOR]

The deep learning-based automatic recognition of the scanning or exposing region in medical imaging automation is a promising new technique, which can decrease the heavy workload of the radiographers, optimize imaging workflow and improve image quality. However, there is little related research and practice in X-ray imaging. In this paper, we focus on two key problems in X-ray imaging automation: automatic recognition of the exposure moment and the exposure region. Consequently, we propose an automatic video analysis framework based on the hybrid model, approaching real-time performance. The framework consists of three interdependent components: Body Structure Detection, Motion State Tracing, and Body Modeling. Body Structure Detection disassembles the patient to obtain the corresponding body keypoints and body Bboxes. Combining and analyzing the two different types of body structure representations is to obtain rich spatial location information about the patient body structure. Motion State Tracing focuses on the motion state analysis of the exposure region to recognize the appropriate exposure moment. The exposure region is calculated by Body Modeling when the exposure moment appears. A large-scale dataset for X-ray examination scene is built to validate the performance of the proposed method. Extensive experiments demonstrate the superiority of the proposed method in automatically recognizing the exposure moment and exposure region. This paradigm provides the first method that can enable automatically and accurately recognize the exposure region in X-ray imaging without the help of the radiographer. [ABSTRACT FROM AUTHOR]

Manual inspection of baggage X-ray images for the detection of threat materials such as explosives is a tedious and stressful task. We have presented a convolutional neural network (CNN)-based approach for automated detection of threat materials in X-ray images. A deep convolutional generative adversarial network (DCGAN)-based data synthesized model is used for the generation of datasets containing gray images (DXBD1) and pseudo-colored images (DXBD2). A deep CNN-based model, named dual X-ray Baggage (DXB), is proposed for automated classification of threat materials in baggage X-ray images. The proposed model consists of seven layers comprising two convolution layers and five fully connected layers. With this model, accuracies of 98% for gray images with F1 score of 0.97 and 99% for pseudo-colored images with F1 score of 0.99 have been achieved. The proposed model requires the classification time of about 12 s and 6 s, respectively, for gray and pseudo-colored images indicating its suitability for real-time monitoring. [ABSTRACT FROM AUTHOR]

Spectral computed tomography (CT) reconstructs images from different spectral data through photon counting detectors (PCDs). However, due to the limited number of photons and the counting rate in the corresponding spectral segment, the reconstructed spectral images are usually affected by severe noise. In this paper, we propose a fourth-order nonlocal tensor decomposition model for spectral CT image reconstruction (FONT-SIR). To maintain the original spatial relationships among similar patches and improve the imaging quality, similar patches without vectorization are grouped in both spectral and spatial domains simultaneously to form the fourth-order processing tensor unit. The similarity of different patches is measured with the cosine similarity of latent features extracted using principal component analysis (PCA). By imposing the constraints of the weighted nuclear and total variation (TV) norms, each fourth-order tensor unit is decomposed into a low-rank component and a sparse component, which can efficiently remove noise and artifacts while preserving the structural details. Moreover, the alternating direction method of multipliers (ADMM) is employed to solve the decomposition model. Extensive experimental results on both simulated and real data sets demonstrate that the proposed FONT-SIR achieves superior qualitative and quantitative performance compared with several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In X-ray imaging, photons are transmitted through and absorbed by the target object, but are also scattered in significant quantities. Previous attempts to use scattered X-ray photons for imaging applications used pencil or fan beam illumination. Here we present 3D X-ray Scatter Tomography using full-field illumination for small-animal imaging. Synchrotron imaging experiments were performed on a phantom and the chest of a juvenile rat. Transmitted and scattered photons were simultaneously imaged with separate cameras; a scientific camera directly downstream of the sample stage, and a pixelated detector with a pinhole imaging system placed at $45{}^\circ $ to the beam axis. We obtained scatter tomogram feature fidelity sufficient for segmentation of the lungs and major airways in the rat. The image contrast in the scatter tomogram slices approached that of transmission imaging, indicating robustness to the amount of multiple scattering present in our case. This opens the possibility of augmenting full-field 2D imaging systems with additional scatter detectors to obtain complementary modes or to improve the fidelity of existing images without additional dose, potentially leading to single-shot or reduced-angle tomography or overall dose reduction for live animal studies. [ABSTRACT FROM AUTHOR]

Image representation is a fundamental task in computer vision. However, most of the existing approaches for image representation ignore the relations between images and consider each input image independently. Intuitively, relations between images can help to understand the images and maintain model consistency over related images, leading to better explainability. In this paper, we consider modeling the image-level relations to generate more informative image representations, and propose ImageGCN, an end-to-end graph convolutional network framework for inductive multi-relational image modeling. We apply ImageGCN to chest X-ray images where rich relational information is available for disease identification. Unlike previous image representation models, ImageGCN learns the representation of an image using both its original pixel features and its relationship with other images. Besides learning informative representations for images, ImageGCN can also be used for object detection in a weakly supervised manner. The experimental results on 3 open-source x-ray datasets, ChestX-ray14, CheXpert and MIMIC-CXR demonstrate that ImageGCN can outperform respective baselines in both disease identification and localization tasks and can achieve comparable and often better results than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Chest X-ray is an important imaging method for the diagnosis of chest diseases. Chest radiograph diagnostic quality assessment is vital for the diagnosis of the disease because unqualified radiographs have negative impacts on doctors’ diagnosis and thus increase the burden on patients due to the re-acquirement of the radiographs. So far no algorithms and public data sets have been developed for chest radiograph diagnostic quality assessment. Towards effective chest X-ray diagnostic quality assessment, we analyze the image characteristics of four main chest radiograph diagnostic quality issues, i.e. Scapula Overlapping Lung, Artifact, Lung Field Loss, and Clavicle Unflatness. Our experiments show that general image classification methods are not competent for the task because the detailed information used for quality assessment by radiologists cannot be fully exploited by deep CNNs and image-level annotations. Then we propose to leverage a multi-label semantic segmentation framework to find the problematic regions, and then classify the quality issues based on the results of segmentation. However, subsequent classification is often negatively affected by certain small segmentation errors. Therefore, we propose to estimate a distance map that measures the distance from a pixel to its nearest segment, and use it to force the prediction of semantic segmentation more holistic and suitable for classification. Extensive experiments validate the effectiveness of our semantic-segmentation-based solution for chest X-ray diagnostic quality assessment. However, general segmentation-based algorithms requires fine pixel-wise annotations in the era of deep learning. In order to reduce reliance on fine annotations and further validate how important pixel-wise annotations are, weak supervision for segmentation is applied, and demonstrates its ability close to that of full supervision. Finally, we present ChestX-rayQuality, a chest radiograph data set, which comprises 480 frontal-view chest radiographs with semantic segmentation annotations and four labels of quality issue. Also, other 1212 chest radiographs with limited annotations are imported to validate our algorithms and arguments on larger data set. These two data set will be made publicly available. [ABSTRACT FROM AUTHOR]

When deep neural network (DNN) was first introduced to the medical image analysis community, researchers were impressed by its performance. However, it is evident now that a large number of manually labeled data is often a must to train a properly functioning DNN. This demand for supervision data and labels is a major bottleneck in current medical image analysis, since collecting a large number of annotations from experienced experts can be time-consuming and expensive. In this paper, we demonstrate that the eye movement of radiologists reading medical images can be a new form of supervision to train the DNN-based computer-aided diagnosis (CAD) system. Particularly, we record the tracks of the radiologists’ gaze when they are reading images. The gaze information is processed and then used to supervise the DNN’s attention via an Attention Consistency module. To the best of our knowledge, the above pipeline is among the earliest efforts to leverage expert eye movement for deep-learning-based CAD. We have conducted extensive experiments on knee X-ray images for osteoarthritis assessment. The results show that our method can achieve considerable improvement in diagnosis performance, with the help of gaze supervision. [ABSTRACT FROM AUTHOR]

In this paper, we focus on X-ray images (X-radiographs) of paintings with concealed sub-surface designs (e.g., deriving from reuse of the painting support or revision of a composition by the artist), which therefore include contributions from both the surface painting and the concealed features. In particular, we propose a self-supervised deep learning-based image separation approach that can be applied to the X-ray images from such paintings to separate them into two hypothetical X-ray images. One of these reconstructed images is related to the X-ray image of the concealed painting, while the second one contains only information related to the X-ray image of the visible painting. The proposed separation network consists of two components: the analysis and the synthesis sub-networks. The analysis sub-network is based on learned coupled iterative shrinkage thresholding algorithms (LCISTA) designed using algorithm unrolling techniques, and the synthesis sub-network consists of several linear mappings. The learning algorithm operates in a totally self-supervised fashion without requiring a sample set that contains both the mixed X-ray images and the separated ones. The proposed method is demonstrated on a real painting with concealed content, Do na Isabel de Porcel by Francisco de Goya, to show its effectiveness. [ABSTRACT FROM AUTHOR]

Since the invention of the X-ray in 1895, biomedical imaging has come a long way and the pace of advances in the field are only accelerating. Research groups are progressing on many fronts through improvements to existing technologies and the development of novel imaging devices and algorithms that not only deliver better pictures, but also more—and more useful—information from the image data. [ABSTRACT FROM AUTHOR]

Robot-assisted medical imaging entails the use of a robot to acquire a medical image. Examples include robot-assisted endoscopic camera imaging, ultrasound imaging where the transducer is held by a robot, X-ray imaging where the source and detector are positioned by robots, and actuated capsule endoscopy, where the capsule is maneuvered by external magnetic actuation. Robot assistance enables the controlled trajectory of the imaging system with high precision and accuracy. This makes it possible to compound acquisitions for increased aperture and for volumetric or tomographic imaging, to track medical instrumentation, and to adjust imaging trajectory in a feedback loop as a function of the patient. Intraoperative robotic medical imaging can provide valuable information to the physician and facilitates the registration of preoperative imaging to the patient. In this review article, we describe some of the robotic imaging systems developed for diagnosis and intervention guidance. In particular, based on the surveyed research activity, we will describe our view of the state of the art in ultrasound, endoscopy, X-ray, optical coherence tomography, and nuclear medicine. We will discuss approaches to autonomous scanning and physics-driven approaches such as elastography and photoacoustic tomography, where the accurate placement and trajectory control of the imaging system using a robot are of paramount importance. We will map out the current state of the art and discuss potential avenues of research. [ABSTRACT FROM AUTHOR]

One of the key challenges to the X-ray security check is to detect the overlapped items in backpacks or suitcases in the X-ray images. Most existing methods improve the robustness of models to the object overlapping problem by enhancing the underlying visual information such as colors and edges. However, this strategy ignores the situations that the objects have similar visual clues as to the background, and objects overlapping each other. Since the two cases rarely appear in existing datasets, we contribute a novel dataset – Cutters and Liquid Containers X-ray Dataset (CLCXray) to complete the related research. Furthermore, we propose a novel Label-aware Mechanism (LA) to tackle the object overlapping problem. Particularly, LA establishes the associations between feature channels and different labels and adjusts the features according to the assigned labels (or pseudo labels) to help improve the prediction results. Extensive experiments demonstrate that the LA is accurate and robust to detect overlapped objects, and also validate the effectiveness and the good generalization of the LA for arbitrary state-of-the-art (SOTA) methods. Furthermore, experimental results show that the network constructed by the LA is superior to the SOTA models on OPIXray and CLCXray, especially solving the challenges of the subset of the highly overlapped objects. [ABSTRACT FROM AUTHOR]

Accurate and robust cephalometric image analysis plays an essential role in orthodontic diagnosis, treatment assessment and surgical planning. This paper proposes a novel landmark localization method for cephalometric analysis using multiscale image patch-based graph convolutional networks. In detail, image patches with the same size are hierarchically sampled from the Gaussian pyramid to well preserve multiscale context information. We combine local appearance and shape information into spatialized features with an attention module to enrich node representations in graph. The spatial relationships of landmarks are built with the incorporation of three-layer graph convolutional networks, and multiple landmarks are simultaneously updated and moved toward the targets in a cascaded coarse-to-fine process. Quantitative results obtained on publicly available cephalometric X-ray images have exhibited superior performance compared with other state-of-the-art methods in terms of mean radial error and successful detection rate within various precision ranges. Our approach performs significantly better especially in the clinically accepted range of 2 mm and this makes it suitable in cephalometric analysis and orthognathic surgery. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (GAN) have shown great potential for image quality improvement in low-dose CT (LDCT). In general, the shallow features of generator include more shallow visual information such as edges and texture, while the deep features of generator contain more deep semantic information such as organization structure. To improve the network's ability to categorically deal with different kinds of information, this paper proposes a new type of GAN with dual-encoder- single-decoder structure. In the structure of the generator, firstly, a pyramid non-local attention module in the main encoder channel is designed to improve the feature extraction effectiveness by enhancing the features with self-similarity; Secondly, another encoder with shallow feature processing module and deep feature processing module is proposed to improve the encoding capabilities of the generator; Finally, the final denoised CT image is generated by fusing main encoder's features, shallow visual features, and deep semantic features. The quality of the generated images is improved due to the use of feature complementation in the generator. In order to improve the adversarial training ability of discriminator, a hierarchical-split ResNet structure is proposed, which improves the feature's richness and reduces the feature's redundancy in discriminator. The experimental results show that compared with the traditional single-encoder- single-decoder based GAN, the proposed method performs better in both image quality and medical diagnostic acceptability. Code is available in https://github.com/hanzefang/DESDGAN. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) scan that uses two distinct X-ray spectra in measurement allows for a dual-energy material decomposition (DEMD). DEMD performs decomposition of the two basis materials used in various imaging applications to improve clinical diagnosis, but it requires specialized CT scanners. Recently, single-energy material decomposition (SEMD) based on X-ray path-length estimation yielded the same results as DEMD when the signal errors are nonexistent in the measurements from the objects composed of only two materials. In SEMD, the decomposition is performed using pregenerated look-up tables containing material-specific values as a function of the measurements and X-ray path lengths. The use of these tables, however, is time-consuming, and the results may not be accurate when measurements are degraded by electronic noise and photon starvation. In this study, we propose an improved SEMD method to provide more accurate material decomposition using conventional CT scanners. The proposed SEMD performs the decomposition based on the physical model of X-ray imaging with the use of measurements and estimated path lengths. Material-specific CT images are reconstructed using a filtered-back-projection algorithm. The performance of the proposed method is evaluated on both simulated data and experimental measurements. This method in practical CT scans yielded material decomposition results similar to that elicited by DEMD. Thus, it can be a viable option to provide material-specific information for clinical diagnosis using conventional CT scanners. [ABSTRACT FROM AUTHOR]

Here, we report a broadband graphene-silicon field-effect coupled detector (FCD) sensitive to photons spanning from soft X-ray to Near-Infrared region. Unlike traditional charge-coupled devices (CCD) relying on serial charge transfer between potential wells, the FCD integrates photo-sensing, charge integration, field-effect amplification, and random readout in one pixel. The charge integration in the potential well of the semiconductor substrate and field-effect coupled amplification in graphene transistor result in a highly sensitive broadband response. In the X-ray region, the device displays a high sensitivity of 1088 CGyair−1cm−2 and a fast response time < ~5 ms. Linear array imaging reveals the potential of our devices for array-based broadband imaging. Our FCD offers a viable strategy to monolithically integrate 2D materials into conventional solid-state imaging technology, exploring the next-generation broadband photodetectors for high-quality imaging and super vision technology. [ABSTRACT FROM AUTHOR]

2D/3D registration that achieves high accuracy and real-time computation is one of the enabling technologies for radiotherapy and image-guided surgeries. Recently, the Convolutional Neural Network (CNN) has been explored to significantly improve the accuracy and efficiency of 2D/3D registration. A pair of intraoperative 2-D x-ray images and synthetic data from pre-operative volume are often required to model the nonconvex mappings between registration parameters and image residual. However, a large clinical dataset collection with accurate poses for x-ray images can be very challenging or even impractical, while exclusive training on synthetic data can frequently cause performance degradation when tested on x-rays. Thus, we propose to train a model on source domain (i.e., synthetic data) to build appearance-pose relationship first and then use an unsupervised cross-modality domain adaptation network (UCMDAN) to adapt the model to target domain (i.e., X-rays) through adversarial learning. We propose to narrow the significant domain gap by alignment in both pixel and feature space. In particular, the image appearance transformation and domain-invariance feature learning by multiple aspects are conducted synergistically. Extensive experiments on CT and CBCT dataset show that the proposed UCMDAN outperforms the existing state-of-the-art domain adaptation approaches. [ABSTRACT FROM AUTHOR]

An X-ray fluorescence (XRF) imaging system is a material analysis system that can represent the material distribution and types of elements by detecting characteristic X-rays emitted from each element. A CdTe semiconductor detector array whose detection efficiency is significantly higher than a silicon drift detector (SDD) is utilized with a deep learning method to improve the energy spectral analysis. In this study, deep learning models for material discrimination and quantitation were applied based on 20 000 energy spectra obtained from Fe, Ni, Cu, and Zn rod phantoms, and a brass phantom was analyzed to verify that Cu, Zn, and brass can be distinguished from each other and that the amount of Cu and Zn in each phantom can be quantitatively analyzed. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 threatens the lives and property safety of countless people and brings a tremendous pressure to health care systems worldwide. The principal challenge in the fight against this disease is the lack of efficient detection methods. AI-assisted diagnosis based on deep learning can detect COVID-19 cases for chest X-ray images automatically, and also improve the accuracy and efficiency of doctors’ diagnosis. However, large scale annotation of chest X-ray images is difficult because of limited resources and heavy burden on the medical system. To meet the challenge, we propose a capsule network model with multi-head attention routing algorithm, called MHA-CoroCapsule, to provide fast and accurate diagnostics for COVID-19 diseases from chest X-ray images. The MHA-CoroCapsule consists of convolutional layers, two capsule layers, and a non-iterative, parameterized multi-head attention routing algorithm is used to quantify the relationship between the two capsule layers. The experiments are performed on a combined dataset constituted by two publicly available datasets including normal, non-COVID pneumonia and COVID-19 images. The model achieves the accuracy of 97.28%, recall of 97.36%, and precision of 97.38% even with a limited number of samples. The experimental results demonstrate that, contrary to the transfer learning and deep feature extraction approaches, the proposed MHA-CoroCapsule has an encouraging performance with fewer trainable parameters and does not require pretraining and plenty of training samples. [ABSTRACT FROM AUTHOR]

The assessment of margin involvement is a fundamental task in breast conserving surgery to prevent recurrences and reoperations. It is usually performed through histology, which makes the process time consuming and can prevent the complete volumetric analysis of large specimens. X-ray phase contrast tomography combines high resolution, sufficient penetration depth and high soft tissue contrast, and can therefore provide a potential solution to this problem. In this work, we used a high-resolution implementation of the edge illumination X-ray phase contrast tomography based on “pixel-skipping” X-ray masks and sample dithering, to provide high definition virtual slices of breast specimens. The scanner was originally designed for intra-operative applications in which short scanning times were prioritised over spatial resolution; however, thanks to the versatility of edge illumination, high-resolution capabilities can be obtained with the same system simply by swapping x-ray masks without this imposing a reduction in the available field of view. This makes possible an improved visibility of fine tissue strands, enabling a direct comparison of selected CT slices with histology, and providing a tool to identify suspect features in large specimens before slicing. Combined with our previous results on fast specimen scanning, this works paves the way for the design of a multi-resolution EI scanner providing intra-operative capabilities as well as serving as a digital pathology system. [ABSTRACT FROM AUTHOR]

Image regression tasks for medical applications, such as bone mineral density (BMD) estimation and left-ventricular ejection fraction (LVEF) prediction, play an important role in computer-aided disease assessment. Most deep regression methods train the neural network with a single regression loss function like MSE or L1 loss. In this paper, we propose the first contrastive learning framework for deep image regression, namely AdaCon, which consists of a feature learning branch via a novel adaptive-margin contrastive loss and a regression prediction branch. Our method incorporates label distance relationships as part of the learned feature representations, which allows for better performance in downstream regression tasks. Moreover, it can be used as a plug-and-play module to improve performance of existing regression methods. We demonstrate the effectiveness of AdaCon on two medical image regression tasks, i.e., bone mineral density estimation from X-ray images and left-ventricular ejection fraction prediction from echocardiogram videos. AdaCon leads to relative improvements of 3.3% and 5.9% in MAE over state-of-the-art BMD estimation and LVEF prediction methods, respectively. [ABSTRACT FROM AUTHOR]

To address the problem of automatic identification of fine-grained fracture types, in this paper, we propose a novel framework using 3D convolutional neural network (CNN) to learn fracture features from voxelized bone models which are obtained by establishing isomorphic mapping from fractured bones to a voxelized template. The network, which is named FractureNet, consists of four discriminators forming a multi-stage hierarchy. Each discriminator includes multiple sub-classifiers. These sub-classifiers are chained by two kinds of feature chains (feature map chain and classification feature chain) in the form of a full m-ary tree to perform multi-stage classification tasks. The features learned and classification results obtained at previous stages serve as prior knowledge for current learning and classification. All sub-classifiers are jointly learned in an end-to-end network via a multi-stage loss function integrating losses of the four discriminators. To make our FractureNet more robust and accurate, a data augmentation strategy termed r-combination with constraints is further proposed on the basis of an adjacency relation and a continuity relation between voxels to create a large-scale fracture dataset of voxel models. Extensive experiments show that the proposed method can recognize various fracture types in patients accurately and effectively, and enables significant improvements over the state-of-the-arts on a variety of fracture recognition tasks. Moreover, ancillary experiments on the CIFAR-10 and the PadChest datasets at large scales further support the superior performance of the proposed FractureNet. [ABSTRACT FROM AUTHOR]

In an attempt to reduce the infection rate of the COrona VIrus Disease-19 (Covid-19) countries around the world have echoed the exigency for an economical, accessible, point-of-need diagnostic test to identify Covid-19 carriers so that they (individuals who test positive) can be advised to self isolate rather than the entire community. Availability of a quick turn-around time diagnostic test would essentially mean that life, in general, can return to normality-at-large. In this regards, studies concurrent in time with ours have investigated different respiratory sounds, including cough, to recognise potential Covid-19 carriers. However, these studies lack clinical control and rely on Internet users confirming their test results in a web questionnaire (crowdsourcing) thus rendering their analysis inadequate. We seek to evaluate the detection performance of a primary screening tool of Covid-19 solely based on the cough sound from 8,380 clinically validated samples with laboratory molecular-test (2,339 Covid-19 positive and 6,041 Covid-19 negative) under quantitative RT-PCR (qRT-PCR) from certified laboratories. All collected samples were clinically labelled, i.e., Covid-19 positive or negative, according to the results in addition to the disease severity based on the qRT-PCR threshold cycle (Ct) and lymphocytes count from the patients. Our proposed generic method is an algorithm based on Empirical Mode Decomposition (EMD) for cough sound detection with subsequent classification based on a tensor of audio sonographs and deep artificial neural network classifier with convolutional layers called ‘DeepCough’. Two different versions of DeepCough based on the number of tensor dimensions, i.e., DeepCough2D and DeepCough3D, have been investigated. These methods have been deployed in a multi-platform prototype web-app ‘CoughDetect’. Covid-19 recognition results rates achieved a promising AUC (Area Under Curve) of $98.80\% \pm 0.83\%$ 98. 80 % ± 0. 83 % , sensitivity of $96.43\% \pm 1.85\%$ 96. 43 % ± 1. 85 % , and specificity of $96.20\% \pm 1.74\%$ 96. 20 % ± 1. 74 % and average AUC of $81.08\% \pm 5.05\%$ 81. 08 % ± 5. 05 % for the recognition of three severity levels. Our proposed web tool as a point-of-need primary diagnostic test for Covid-19 facilitates the rapid detection of the infection. We believe it has the potential to significantly hamper the Covid-19 pandemic across the world. [ABSTRACT FROM AUTHOR]

Currently, the world is experiencing the rapid spread of Coronavirus Disease 2019 (COVID-19). Since the epidemic continues to take a devastating impact on the society, economy, and healthcare, the real-time detection of COVID-19 is essential for fast and cost-effective diagnosis services. Fortunately, deep learning (DL), as a promising technology, enables the COVID-19 diagnosis services on chest X-ray (CXR) images. The training task of DL model is generally implemented at the centralized cloud. However, due to the geo-distributed data sources and the transmission of large amounts of raw data to the centralized cloud, the transmission latency becomes a bottleneck of the COVID-19 diagnosis model training. In this paper, we propose a Distributed COVID-19 detection model training method on CXR images with edge-cloud collaboration, named DisCOV. Specifically, to improve the training efficiency and guarantee the model accuracy, a distributed lightweight model-based training algorithm is designed with the cooperation of edge computing and cloud computing. In addition, a resource allocation algorithm is developed during the training to jointly minimize the time cost and energy consumption. Extensive experiments based on real-world CXR image datasets demonstrate that DisCOV is better performed and more promising than the existing baselines. [ABSTRACT FROM AUTHOR]

Automated multi-label chest X-ray (CXR) image classification has recently made significant progress in clinical diagnosis based on the advanced deep learning techniques. However, most existing methods mainly focus on analyzing locality visual cues from a single image but fail to leverage the underlying explicit correlations among different images for precise disease diagnosis. By contrast, an experienced radiologist expertizes in transferring knowledge from previous tasks to diagnose the present radiograph. To enable the machine like a radiologist, this paper proposes a novel Semantic Similarity Graph Embedding (SSGE) framework, which explicitly explores the semantic similarities among images to optimize the visual feature embedding for improving the performance of multi-label CXR images classification. Specifically, the proposed SSGE framework contains three main components: the image feature embedding (IFE) module, similarity graph construction (SGC) module, and semantic similarity learning (SSL) module. To realize interactive teaching and learning between visual and semantic information, the proposed SSGE framework is built on the “Teacher-Student” (semantic-visual) learning mechanism. With the guidance and supervision of the cross-image similarity graph generated by the SGC module, the SSL module leverages Graph Convolutional Network (GCN) to adaptively recalibrate the multi-image feature representations extracted from the IFE module, which guarantees their semantic consistency. Furthermore, we propose a novel re-weighting strategy to learn a more optimal semantic-similarity graph for the information propagation of the GCN layers. Extensive experiments on two benchmark datasets demonstrate the effectiveness of the proposed method in comparison with some state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Because of its fast scintillation peaked at 220 nm with a decay time of less than 0.6 ns, barium fluoride crystals have attracted broad interest in the community pursuing ultrafast detectors for future high energy physics (HEP) experiments and GHz hard X-ray imaging for future X-ray free-electron laser (XFEL) facilities. A crucial issue for its application is the spectral response of photodetectors down to vacuum ultraviolet (VUV). In this article, we compare quantum efficiency (QE) and photon detection efficiency (PDE) spectra measured for several ultraviolet (UV) photodetectors down to 200 nm. Their figures of merit on the detection efficiency for the fast component and the suppression of the slow component are also discussed. [ABSTRACT FROM AUTHOR]

High-resolution X-ray imaging techniques, usually known as microradiography and micro-computed tomography (CT), have become highly required and frequently used tools for biology, biomedical and preclinical research. State-of-the-art micro-CT scanners are capable of achieving a spatial resolution of few micrometers or even less, thanks to the constant development of compact microfocus X-ray sources together with simultaneous progress in detector technologies. The current standard in X-ray detection is a digital imaging device containing read-out circuitry coupled with a scintillation sensor. Such detectors are available in a variety of different sizes, and are easy to use and relatively affordable. Nevertheless, the mentioned technology suffers from inherent limitations like, for example, undesirable generation of dark current, that compromise the quality of the provided data. This work demonstrates the applicability of Timepix large-area hybrid-pixel photon-counting detectors (PCDs) for high-resolution X-ray imaging in biology research. Photon-counting detection technology provides dark-current-free quantum-counting operation. Therefore, an enhanced contrast-to-noise ratio of the acquired data is achieved. Furthermore, the biased semiconductor sensor achieves almost ideal point-spread function (PSF) resulting in images with high spatial-resolution. Both above-mentioned features make PCDs to be excellent tools for high-resolution X-ray imaging especially for samples with low intrinsic absorption contrast. We evaluated the imaging performance of large-area Timepix detectors compared to a state-of-the-art flat-panel detector dedicated for high-resolution X-ray imaging. The presented data demonstrate the versatility of the used detectors as it covers a wide range of samples from laboratory animals to single-cell marine organisms. [ABSTRACT FROM AUTHOR]