Search

Due to the instability of the hyperspectral imaging system and the atmospheric interference, hyperspectral images (HSIs) often suffer from losing the image information of areas with different shapes, which significantly degrades the data quality and further limits the effectiveness of methods for subsequent tasks. Although mainstream deep learning-based methods have achieved promising inpainting performance, the complicated ground object distributions increase the difficulty of HSIs inpainting in practice. In addition, spectral redundancy and complex texture details are two main challenges for deep neural network-based inpainting methods. To address the above issues, we propose a Global-Local Constraints-based Spectral Adaptive Network (GLCSA-Net) for HSI inpainting. To reduce the redundancy of spectral information, a multi-frequency channel attention module is designed to strengthen the essential channels and suppress the less significant ones, which calculates adaptive weight coefficients by converting feature maps to the frequency domain. Furthermore, we propose to constrain the generation of missing areas from both global and local perspectives, by fully leveraging the HSI texture information, so that the overall structure information and regional texture consistency of the original HSI can be maintained. The proposed method has been extensively evaluated on the Indian Pines and FCH datasets. The promising results indicate that GLCSA-Net outperforms the state-of-the-art methods in quantitative and qualitative assessments. [ABSTRACT FROM AUTHOR]

Automatically identifying objects similar to the surroundings is a complex and difficult task in real-world scenarios. In addition to the high intrinsic similarity between camouflaged objects and their backgrounds, these objects are usually diverse in scale and blurred in appearance. And the deceptive nature of the camouflaged objects introduces lots of noise into the features and generates inaccurate segmentation map extracted by deep learning model. We tackle these problems by proposing a novel dynamic interactive refinement network (DIRNet), which aims to make the features exploit effective details and semantics together as well as discard interference information. Specifically, we utilize bilateral interaction module (BIM) to interact with foreground and background information to conduct contextual exploration, which can capture more meaningful details and refine the confusion. Additionally, in the purpose of retaining the appropriate information and erasing noise, we design an adjacent aggregation interaction module (AAIM) to integrate the adjacent multi-level features with attention coefficients for each layer. The final results are obtained through the dynamic refinement of the BIM and AAIM. Extensive quantitative and qualitative experiments on four public benchmark datasets demonstrate that our proposed DIRNet is an effective COD framework and outperforms 14 state-of-the-art models. [ABSTRACT FROM AUTHOR]

Image alignment is a highly researched topic in computer vision, which aligns a pair of images due to image changes. Despite the numerous studies conducted on this topic, large object transformation and huge illumination changes between a pair of images are still commonly encountered in real-world scenes, making the task of image alignment very challenging. In this paper, a novel image alignment algorithm is proposed. By inputting a pair of images that need to be aligned into the correlated siamese neural network, a series of blocks are extracted in feature layers from the reference image, and those blocks are correlated in the feature layers of the target image. Finally, the homography parameters between images are then regressed from the correlate layers. Compared with the classical image alignment algorithms, supervised deep homography, and unsupervised deep homography, the experimental results of our method demonstrate a superior performance on the image alignment tasks involving illumination changes, camera translation, and rotation. [ABSTRACT FROM AUTHOR]

Most of the cutting-edge 3D convolutional-based video saliency prediction models adopt a fully convolutional encoder-decoder architecture, which provides a good spatiotemporal representation of salient regions. However, the spatiotemporal cues of the saliency models will be continuously diluted during the decoding process, leading to a decreased ability to locate salient regions. To address this limitation, we propose a simple and effective gated fusion network (GFNet) to conduct video saliency prediction. Specifically, GFNet is built on a fully 3D convolutional encoder-decoder architecture and consists of a key component named the gated fusion (GF) module, which acts as a message screening unit between each level encoder and decoder features. In the GF module, the gate can be obtained via the combination of features from the previous decoder block and the current encoder block, which is employed to weight the encoder features. In this way, GFNet can control the message passing between each level encoder and decoder block. Extensive experimental results on four video saliency datasets show that our method achieves comparable performance against state-of-the-art models. The code is available at https://github.com/wusonghe/GFNet. [ABSTRACT FROM AUTHOR]

To better understand the acid-temperature coupling mechanism, it is critical to quantify the coupled effects of acid and temperature on the damage characteristics of sandstone. In this paper, macroscopic mechanical properties and microstructure changes of sandstone samples under different acid-temperature conditions are obtained by uniaxial compression experiments and nuclear magnetic resonance (NMR) measurements. Stress–strain curves and failure modes, changes in porosity, T2 (transverse relaxation time) spectra and fractal characteristics under different acid-temperature conditions are analyzed. Damage mechanism of acid-temperature coupling is discussed and damage variables caused by acid and temperature are defined, respectively. The results show that the effects of acid and temperature on the failure modes of sandstone samples are different. Based on the T2 spectral morphology of samples, the pores are classified into micro pores (< 0.1 μm), meso pores (0.1–1 μm) and macro pores (≥ 1 μm). With the increase of acidity, the sum of micro pores and meso pores gradually increase while macro pores gradually decrease. With increasing temperature, micro pores tend to decrease while meso pores and macro pores increase gradually. The fractal dimension of macro pores is largest, followed by the overall pore structure and meso pores. The damage variable caused by physical effect Dp is greater than that caused by chemical effect Dc. There is an exponential decreasing relationship between σc, E and total damage variable D. Highlights: Quantification of coupled effects of acid-temperature on the damage characteristics of sandstone. Changes of microscopic characteristics on the effects of acid-temperature coupling. Definition of damage variables separately for the effects of acid and temperature. [ABSTRACT FROM AUTHOR]

Bokeh effect is usually used to highlight major contents in an image. Limited by the small sensors, cameras on smartphones are less sensitive to the depth information and cannot directly produce bokeh effect as pleasant as digital single lens reflex cameras. To address this problem, a depth-guided deep filtering network, called DDFN, is proposed in this study. Specifically, the focused region detection block is designed to detect the salient areas, and the depth estimated block is introduced to estimate depth maps from full-focus images. Further, combining depth maps and focused features, an adaptive rendering block is proposed to synthesize bokeh effect with adaptive cross 1-D filters. Both quantitative and qualitative evaluations on the public datasets demonstrate that the proposed model performs favorably against state-of-the-art methods in terms of rendering effects and has lower computational cost, e.g., 24.07 dB PSNR on EBB! dataset and 0.45 s inference times for a 512 × 768 image on a Snapdragon 865 mobile processor. [ABSTRACT FROM AUTHOR]

The new generation versatile video coding (VVC) standard brings extremely high compression efficiency. Meanwhile, the complexity of the encoder is also greatly increased. In view of this, this paper proposes a fast algorithm for all zero block detection to speed up the quantization process, thereby reducing the complexity of the encoder. The method proposed in this paper consists of three parts. Firstly, genuine all zero blocks are detected based on a fixed threshold which is derived by hard decision quantization formula. Secondly, parts of pseudo all zero blocks are detected based on an adaptive threshold which is derived by analyzing the positions of transform coefficients under a certain condition. Finally, for the remaining pseudo all zero blocks, machine learning is introduced and the decision is made through the fully connected neural network. Experimental results show that the proposed fast algorithm achieves up to 7.505% and 7.049% coding time saving under Low Delay B and Random Access configurations with only 0.470% and 0.578% performance loss on average, respectively. [ABSTRACT FROM AUTHOR]

Video stitching is a technique that stitches multiple overlapped videos acquired from different cameras, which is widely applied in many applications, including video surveillance, autonomous driving, and virtual reality. Feature-based stitching methods are popular in this area because of their invariance property and efficiency. However, the video stitching pipeline is relatively complicated and the amount of data calculation is large, which impedes its real-time applications. In this paper, we propose a real-time video stitching framework based on vision Digital Signal Processing (DSP). Real-time processing is achieved by the algorithm-level and system-level optimizations. In the algorithm of ORB feature extraction, methods, including look-up table, linear approximation, and single instruction multiple data (SIMD), are adopted to optimize its computation on DSP. In the algorithm of feature matching, the Hamming distance calculation is eliminated for some feature point pairs when their coordinates and angles are not satisfied with certain conditions. Reverse feature matching is proposed to improve registration accuracy. In system-level optimization, the directed acyclic graph (DAG)-based scheduling is proposed to improve the calculation efficiency on dual DSPs, and a ping-pong buffer is utilized to speed up the data transmission between DSP and external memory. Experimental results show that the proposed method can achieve a ten times speedup than that of the CPU, and it can achieve 1536 × 1024@37fps real-time processing on vision DSP. [ABSTRACT FROM AUTHOR]

Object detection is one of the most challenging tasks in computer vision. With the advances in semiconductor devices and chip technology, hardware accelerators have been widely used. Field-programmable gate arrays (FPGAs) are a highly flexible hardware platform that allows customized reconfiguration of the integrated circuit, which has the potential to improve the efficiency of object detection accelerators. However, few reviews summarize FPGA-based object detection accelerators. Also, there is no general principle for realizing object detection according to FPGA characteristics. In this paper, the current hardware accelerators are introduced and compared. Then, the typical deep learning-based object detectors are summarized. Next, the questions of "Why choose FPGA," "The design goals of FPGA accelerators" and "The design methods for FPGA accelerators" are discussed in detail. Finally, the challenges of object detection algorithms, hardware, and co-design are presented. In addition, an online platform (https://github.com/vivian13maker/) is constructed to provide specific information on all advanced works. [ABSTRACT FROM AUTHOR]

Incremental Learning is a particular form of machine learning that enables a model to be modified incrementally, when new data becomes available. In this way, the model can adapt to the new data without the lengthy and time-consuming process required for complete model re-training. However, existing incremental learning methods face two significant problems: 1) noise in the classification sample data, 2) poor accuracy of modern classification algorithms when applied to modern classification problems. In order to deal with these issues, this paper proposes an integrated classification model, known as a Pre-trained Truncated Gradient Confidence-weighted (Pt-TGCW) model. Since the pre-trained model can extract and transform image information into a feature vector, the integrated model also shows its advantages in the field of image classification. Experimental results on ten datasets demonstrate that the proposed method outperform the original counterparts. [ABSTRACT FROM AUTHOR]

The brain is an energy-consuming organ that heavily relies on the heart for energy supply. Heart abnormalities detected by electrocardiogram (ECG) might provide diagnostic indicators for brain dysfunctions such as stroke. Diagnosis of brain diseases by ECG requires proficient domain knowledge, which is both time and labor consuming. Deep learning is capable of constructing a nonlinear correlation between ECG and stroke without prior expert knowledge. Here, we propose a data-driven classifier-Dense convolutional neural Network (DenseNet) for stroke prediction based on 12-leads ECG data. With our finely-tuned model, we obtain the training accuracy of 99.99% and the prediction accuracy of 85.82%. To our knowledge, this is the first report studying the correlation between stroke and ECG with the aid of deep learning. The results indicate that ECG is a valuable complementary technique for stroke diagnostics. [ABSTRACT FROM AUTHOR]

The recent development of neuroimaging technology and network theory allows us to visualize and characterize the whole-brain functional connectivity in vivo. The importance of conventional structural image atlas widely used in population-based neuroimaging studies has been well verified. Similarly, a "common" brain connectivity map (also called connectome atlas) across individuals can open a new pathway to interpreting disorder-related brain cognition and behaviors. However, the main obstacle of applying the classic image atlas construction approaches to the connectome data is that a regular data structure (such as a grid) in such methods breaks down the intrinsic geometry of the network connectivity derived from the irregular data domain (in the setting of a graph). To tackle this hurdle, we first embed the brain network into a set of graph signals in the Euclidean space via the diffusion mapping technique. Furthermore, we cast the problem of connectome atlas construction into a novel learning-based graph inference model. It can be constructed by iterating the following processes: (1) align all individual brain networks to a common space spanned by the graph spectrum bases of the latent common network, and (2) learn graph Laplacian of the common network that is in consensus with all aligned brain networks. We have evaluated our novel method for connectome atlas construction in comparison with non-learning-based counterparts. Based on experiments using network connectivity data from populations with neurodegenerative and neuropediatric disorders, our approach has demonstrated statistically meaningful improvement over existing methods. [ABSTRACT FROM AUTHOR]

Fifth generation mobile networks (5G) is coming into our life and it will not only provide an increase of 1000 times in Internet traffic in the next decade but will also offer "smarter" user experience. With the commercial uses of 5G, online social networks and smart phones will spring up again. Then cross-modality data will play a more important role in the daily information dissemination. As an effective way of content analysis, topic detection has attracted much research interest, but conventional topic analysis is undergoing the limitations from the cross-modality heterogenous data. This paper proposes a sentimental image dominant graph topic model, that can detect the topic from the heterogenous data and mine the sentiment of each topic. In details, we design a topic model to transfer both the low-level visual modality and the high-level text modality into a semantic manifold, and improve the discriminative power of CNN feature by jointly optimizing the output of both convolutional layer and fully-connected layer. Furthermore, since the sentimental impact is very significant for understanding the intrinsic meaning of topics, we introduce a semantic score of subjective sentences to calculate the sentiment on the base of the contextual sentence structure. The comparison experiments on the public cross-modality benchmark show the promising performance of our model. So our method using AI technology will facilitate the intellectualization of 5G. [ABSTRACT FROM AUTHOR]

This paper presents a novel end-to-end system for real-time indoor scene reconstruction, which outperforms traditional image feature point-based method and dense geometry correspondence-based method in handling indoor scenes with less texture and geometry features. In our method, we fully explore the Manhattan assumption, i.e. scenes are majorly consisted with planar surfaces with orthogonal normal directions. Given an input depth frame, we first extract dominant axes coordinates via principle component analysis which involves the orthogonal prior and reduce the influence of noise. Then we calculate the coordinates of dominant planes (such as walls, floor and ceiling) in the coordinates using mean shift. Finally, we compute the camera orientation and reconstruct the scene by proposing a fast scheme based on matching the dominant axes and planes to the previous frame. We have tested our approach on several datasets and demonstrated that it outperforms some well known existing methods in these experiments. The performance of our method is also able to meet the requirement of real-time with an unoptimized CPU implementation. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are increasingly becoming the focus in a number of researches because abundant studies certify miRNAs play vital roles and have critical functions in various biologic processes. Considering the high cost of experiment research to miRNA-disease association, we explore the way to predict the miRNA-disease association using the extensive collaborative filtering in order to diagnose the diseases better. Specifically, we introduce the prediction model of collaborative filtering-based miRNA-disease association prediction (CFMDA) and verify the model by leave-one-out cross validation(LOOCV) and case validation. The CFMDA considers the miRNA functional similarity and disease similarity while uses minimal amount of related information. CFMDA achieves AUCs of 0.8364 using leave-one-out cross validation, which is the highest AUCs compared to other 5 methods. Meanwhile, we obtain more than 85% confirmation of predicted associations using three kinds of case validations. Generally, our method is faster and more effective than other state-of-the-art methods while it doesn't need any negative samples. [ABSTRACT FROM AUTHOR]

The reconstruction of bioluminescence tomography (BLT) is severely ill-posed due to the insufficient measurements and diffuses nature of the light propagation. Predefined permissible source region (PSR) combined with regularization terms is one common strategy to reduce such ill-posedness. However, the region of PSR is usually hard to determine and can be easily affected by subjective consciousness. Hence, we theoretically developed a filtered maximum likelihood expectation maximization (fMLEM) method for BLT. Our method can avoid predefining the PSR and provide a robust and accurate result for global reconstruction. In the method, the simplified spherical harmonics approximation (SPN) was applied to characterize diffuse light propagation in medium, and the statistical estimation-based MLEM algorithm combined with a filter function was used to solve the inverse problem. We systematically demonstrated the performance of our method by the regular geometry- and digital mouse-based simulations and a liver cancer-based in vivo experiment. Graphical abstract The filtered MLEM-based global reconstruction method for BLT. [ABSTRACT FROM AUTHOR]

The era of human brain science research is dawning. Researchers utilize the various multi-disciplinary knowledge to explore the human brain,such as physiology and bioinformatics. The emerging disease association prediction technology can speed up the study of diseases, so as to better understanding the structure and function of human body. There are increasing evidences that miRNA plays a significant role in nervous system development, adult function, plasticity, and vulnerability to neurological disease states. In this paper,we proposed the novel improved collaborative filtering-based miRNA-disease association prediction (ICFMDA) approach. Known miRNA-disease associations can be viewed as a bipartite network between diseases and miRNAs. ICFMDA defined significance SIG between pairs of diseases or miRNAs to model the preference on the choices of other entities. The collaborative filtering algorithm is further improved by incorporating similarity matrices to enable the prediction for new miRNA or disease without known associations. Potential miRNA-disease associations are scored with the addition of bidirectional recommendation results with low computational cost. ICFMDA achieved a 0.9076 AUC of ROC curve in global leave-one-out cross validation, which outperformed the state-of-the-art models. ICFMDA is a compact and accurate tool for potential miRNA-disease association prediction. We hope that ICFMDA would be useful in future miRNA and brain researches,and achieve better understanding of the nervous system in molecular level, cellular level, cell change process, and thus can support the research of human brain. [ABSTRACT FROM AUTHOR]

MiRNA-disease association is important to disease diagnosis and treatment. Prediction of miRNA-disease associations is receiving increasing attention. Using the huge number of known databases to predict potential associations between miRNAs and diseases is an important topic in the field of biology and medicine. In this paper, we propose a novel computational method of with Short Acyclic Connections in Heterogeneous Graph (SACMDA). SACMDA obtains AUCs of 0.8770 and 0.8368 during global and local leave-one-out cross validation, respectively. Furthermore, SACMDA has been applied to three important human cancers for performance evaluation. As a result, 92% (Colon Neoplasms), 96% (Carcinoma Hepatocellular) and 94% (Esophageal Neoplasms) of top 50 predicted miRNAs are confirmed by recent experimental reports. What’s more, SACMDA could be effectively applied to new diseases and new miRNAs without any known associations, which overcomes the limitations of many previous methods. [ABSTRACT FROM AUTHOR]

Given the tremendous growth of sport fans, the 'Intelligent Arena', which can greatly improve the fun of traditional sports, becomes one of the new-emerging applications and research topics. The development of multimedia computing and artificial intelligence technologies support intelligent sport video analysis to add live video broadcast, score detection, highlight video generation, and online sharing functions to the intelligent arena applications. In this paper, we have proposed a deep learning based video analysis scheme for intelligent basketball arena applications. First of all, with multiple cameras or mobile devices capturing the activities in arena, the proposed scheme can automatically select the camera to give high-quality broadcast in real-time. Furthermore, with basketball energy image based deep conventional neural network, we can detect the scoring clips as the highlight video reels to support the wonderful actions replay and online sharing functions. Finally, evaluations on a built real-world basketball match dataset demonstrate that the proposed system can obtain 94.59 % accuracy with only less than 45 m s processing time (i.e., 10 m s broadcast camera selection, and 35 m s for scoring detection) for each frame. As the outstanding performance, the proposed deep learning based basketball video analysis scheme is implemented into a commercial intelligent basketball arena application named 'Standz Basketball'. Although the application had been only released for one month, it achieves the 85 t h day download ranking place in the sport category of Chinese iTunes market. [ABSTRACT FROM AUTHOR]

Limited memory size is considered as a major bottleneck in data centers for intelligent urban computing. It is shown that there exist a large number of duplicated pages when various processes working with big data are hosted in data centers. Memory deduplication aims to automatically eliminate duplicate data in memory. It is an efficient technique to reduce memory requirement. In memory deduplication, pages with same content are detected and merged into a single copy. Recently, several system-level techniques have been proposed to address this issue, in which content-based page sharing (CBPS) is most widely used, since CBPS can be performed transparently in the hypervisor layer without any modification to guest operating systems of data center. In this paper, we survey several techniques for memory deduplication. We also classify these techniques based on their characteristics to highlight their similarities and differences. The aim of this paper is to provide insights to researchers into working of memory deduplication techniques and also to motivate them to propose better intelligent urban computing systems. [ABSTRACT FROM AUTHOR]

With the rapid growth of the number of images on the Internet, it has become more necessary to ensure the content security of images. The key problem is retrieving relevant images from the large database. Binary embedding is an effective way to improve the efficiency of calculating similarities for image content security as binary code is storage efficient and fast to compute. It tries to convert real-valued signatures into binary codes while preserving similarity of the original data, and most binary embedding methods quantize each projected dimension to one bit (presented as 0/1). As a consequence, it greatly decreases the discriminability of original signatures. In this paper, we first propose a novel triple-bit quantization strategy to solve the problem by assigning 3-bit to each dimension. Then, asymmetric distance algorithm is applied to re-rank candidates obtained from Hamming space for the final nearest neighbors. For simplicity, we call the framework triple-bit quantization with asymmetric distance (TBAD). The inherence of TBAD is combining the best of binary codes and real-valued signatures to get nearest neighbors quickly and concisely. Moreover, TBAD is applicable to a wide variety of embedding techniques. Experimental comparisons on BIGANN set show that the proposed method can achieve remarkable improvement in query accuracy compared to original binary embedding methods. [ABSTRACT FROM AUTHOR]

This paper proposes a 3D laser scanning technology for the pinhole camera with lens distortion. On the basis of the pinhole camera mathematical model, we improve the extraction algorithm of Harris corner points, and introduce the professional 2D RAC calibration, 3D reconstruction, and 3D volume calculation. With the help of stepper motor, and the camera, we can capture a series of images and obtain the object 3D surface information. Furthermore, we use the line laser with Gaussian distribution as the measurement illuminant, and select the samples at the peak of intensity distribution. This brings both high accuracy and speed of measurement. [ABSTRACT FROM AUTHOR]

Although the distance between binary codes can be computed fast in Hamming space, linear search is not practical for large scale datasets. Therefore attention has been paid to the efficiency of performing approximate nearest neighbor search, in which hierarchical clustering trees (HCT) are widely used. However, HCT select cluster centers randomly and build indexes with the entire binary code, this degrades search performance. In this paper, we first propose a new clustering algorithm, which chooses cluster centers on the basis of relative distances and uses a more homogeneous partition of the dataset than HCT has to build the hierarchical clustering trees. Then, we present an algorithm to compress binary codes by extracting distinctive bits according to the standard deviation of each bit. Consequently, a new index is proposed using compressed binary codes based on hierarchical decomposition of binary spaces. Experiments conducted on reference datasets and a dataset of one billion binary codes demonstrate the effectiveness and efficiency of our method. [ABSTRACT FROM AUTHOR]

It is an important task to improve performance for sparse matrix vector multiplication (SpMV), and it is a difficult task because of its irregular memory access. General purpose GPU (GPGPU) provides high computing ability and substantial bandwidth that cannot be fully exploited by SpMV due to its irregularity. In this paper, we propose two novel methods to optimize the memory bandwidth for SpMV on GPGPU. First, a new storage format is proposed to exploit memory bandwidth of GPU architecture more efficiently. The new storage format can ensure that there are as many non-zeros as possible in the format which is suitable to exploit the memory bandwidth of the GPU. Second, we propose a cache blocking method to improve the performance of SpMV on GPU architecture. The sparse matrix is partitioned into sub-blocks that are stored in CSR format.With the blocking method, the corresponding part of vector x can be reused in the GPU cache, so the time to access the global memory for vector x is reduced heavily. Experiments are carried out on three GPU platforms, GeForce 9800 GX2, GeForce GTX 480, and Tesla K40. Experimental results show that both new methods can efficiently improve the utilization of GPU memory bandwidth and the performance of the GPU. [ABSTRACT FROM AUTHOR]

Accumulating evidences have shown that plenty of miRNAs play fundamental and important roles in various biological processes and the deregulations of miRNAs are associated with a broad range of human diseases. However, the mechanisms underlying the dysregulations of miRNAs still have not been fully understood yet. All the previous computational approaches can only predict binary associations between diseases and miRNAs. Predicting multiple types of disease-miRNA associations can further broaden our understanding about the molecular basis of diseases in the level of miRNAs. In this study, the model of Restricted Boltzmann machine for multiple types of miRNA-disease association prediction (RBMMMDA) was developed to predict four different types of miRNA-disease associations. Based on this model, we could obtain not only new miRNA-disease associations, but also corresponding association types. To our knowledge, RBMMMDA is the first model which could computationally infer association types of miRNA-disease pairs. Leave-one-out cross validation was implemented for RBMMMDA and the AUC of 0.8606 demonstrated the reliable and effective performance of RBMMMDA. In the case studies about lung cancer, breast cancer, and global prediction for all the diseases simultaneously, 50, 42, and 45 out of top 100 predicted miRNA-disease association types were confirmed by recent biological experimental literatures, respectively. [ABSTRACT FROM AUTHOR]

Increasing evidence has indicated that plenty of lncRNAs play important roles in many critical biological processes. Developing powerful computational models to construct lncRNA functional similarity network based on heterogeneous biological datasets is one of the most important and popular topics in the fields of both lncRNAs and complex diseases. Functional similarity network consturction could benefit the model development for both lncRNA function inference and lncRNA-disease association identification. However, little effort has been attempted to analysis and calculate lncRNA functional similarity on a large scale. In this study, based on the assumption that functionally similar lncRNAs tend to be associated with similar diseases, we developed two novel lncRNA functional similarity calculation models (LNCSIM). LNCSIM was evaluated by introducing similarity scores into the model of Laplacian Regularized Least Squares for LncRNA-Disease Association (LRLSLDA) for lncRNA-disease association prediction. As a result, new predictive models improved the performance of LRLSLDA in the leave-one-out cross validation of various known lncRNA-disease associations datasets. Furthermore, some of the predictive results for colorectal cancer and lung cancer were verified by independent biological experimental studies. It is anticipated that LNCSIM could be a useful and important biological tool for human disease diagnosis, treatment, and prevention. [ABSTRACT FROM AUTHOR]

Pose-guided person image synthesis is a challenging task that aims to generate photo-realistic images of a person with the same appearance as a source image but the same pose as a target image. Existing methods often suffer from noticeable artifacts due to the omission of multi-view information, and the requirement for paired source–target images in certain methods during training further limits the application of the models. To address these issues, we present a semantic neural person image synthesis framework, named SNPIS, which leverages neural radiance fields (NeRF) to synthesize high-fidelity human images of arbitrary poses from multi-view source images and target semantic maps. First, we introduce the semantic mirror orientation adjustment that forces sampling points to focus on the human body, effectively suppressing background interference and enhancing human details. Then we devise a NeRF-based appearance-shape decoupling generative adversarial network, which separates appearance and shape of a shared volume generated from multi-view source images and corresponding semantic maps. Finally, we use the obtained decoupled generator to synthesize human images guided by the target semantic maps, employing appearance inversion, and optimizing pose reconstruction with semantic consistency constraint. Experimental results show that our approach not only outperforms existing unpaired pose-guided person image synthesis methods, but also competes with many paired methods. [ABSTRACT FROM AUTHOR]

Domain generalization (DG) for medical image segmentation due to privacy preservation prefers learning from a single-source domain and expects good robustness on unseen target domains. To achieve this goal, previous methods mainly use data augmentation to expand the distribution of samples and learn invariant content from them. However, most of these methods commonly perform global augmentation, leading to limited augmented sample diversity. In addition, the style of the augmented image is more scattered than the source domain, which may cause the model to overfit the style of the source domain. To address the above issues, we propose an invariant content representation network (ICRN) to enhance the learning of invariant content and suppress the learning of variability styles. Specifically, we first design a gamma correction-based local style augmentation (LSA) to expand the distribution of samples by augmenting foreground and background styles, respectively. Then, based on the augmented samples, we introduce invariant content learning (ICL) to learn generalizable invariant content from both augmented and source-domain samples. Finally, we design domain-specific batch normalization (DSBN) based style adversarial learning (SAL) to suppress the learning of preferences for source-domain styles. Experimental results show that our proposed method improves by 8.74% and 11.33% in overall dice coefficient (Dice) and reduces 15.88 mm and 3.87 mm in overall average surface distance (ASD) on two publicly available cross-domain datasets, Fundus and Prostate, compared to the state-of-the-art DG methods. The code is available at https://github.com/ZMC-IIIM/ICRN-DG ., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)