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The identification of melanoma involves an integrated analysis of skin lesion images acquired using clinical and dermoscopy modalities. Dermoscopic images provide a detailed view of the subsurface visual structures that supplement the macroscopic details from clinical images. Visual melanoma diagnosis is commonly based on the 7-point visual category checklist (7PC), which involves identifying specific characteristics of skin lesions. The 7PC contains intrinsic relationships between categories that can aid classification, such as shared features, correlations, and the contributions of categories towards diagnosis. Manual classification is subjective and prone to intra- and interobserver variability. This presents an opportunity for automated methods to aid in diagnostic decision support. Current state-of-the-art methods focus on a single image modality (either clinical or dermoscopy) and ignore information from the other, or do not fully leverage the complementary information from both modalities. Furthermore, there is not a method to exploit the ‘intercategory’ relationships in the 7PC. In this study, we address these issues by proposing a graph-based intercategory and intermodality network (GIIN) with two modules. A graph-based relational module (GRM) leverages intercategorical relations, intermodal relations, and prioritises the visual structure details from dermoscopy by encoding category representations in a graph network. The category embedding learning module (CELM) captures representations that are specialised for each category and support the GRM. We show that our modules are effective at enhancing classification performance using three public datasets (7PC, ISIC 2017, and ISIC 2018), and that our method outperforms state-of-the-art methods at classifying the 7PC categories and diagnosis. [ABSTRACT FROM AUTHOR]

Nasopharyngeal Carcinoma (NPC) is a malignant epithelial cancer arising from the nasopharynx. Survival prediction is a major concern for NPC patients, as it provides early prognostic information to plan treatments. Recently, deep survival models based on deep learning have demonstrated the potential to outperform traditional radiomics-based survival prediction models. Deep survival models usually use image patches covering the whole target regions (e.g., nasopharynx for NPC) or containing only segmented tumor regions as the input. However, the models using the whole target regions will also include non-relevant background information, while the models using segmented tumor regions will disregard potentially prognostic information existing out of primary tumors (e.g., local lymph node metastasis and adjacent tissue invasion). In this study, we propose a 3D end-to-end Deep Multi-Task Survival model (DeepMTS) for joint survival prediction and tumor segmentation in advanced NPC from pretreatment PET/CT. Our novelty is the introduction of a hard-sharing segmentation backbone to guide the extraction of local features related to the primary tumors, which reduces the interference from non-relevant background information. In addition, we also introduce a cascaded survival network to capture the prognostic information existing out of primary tumors and further leverage the global tumor information (e.g., tumor size, shape, and locations) derived from the segmentation backbone. Our experiments with two clinical datasets demonstrate that our DeepMTS can consistently outperform traditional radiomics-based survival prediction models and existing deep survival models. [ABSTRACT FROM AUTHOR]

Positron Emission Tomography (PET) has become a preferred imaging modality for cancer diagnosis, radiotherapy planning, and treatment responses monitoring. Accurate and automatic tumor segmentation is the fundamental requirement for these clinical applications. Deep convolutional neural networks have become the state-of-the-art in PET tumor segmentation. The normalization process is one of the key components for accelerating network training and improving the performance of the network. However, existing normalization methods either introduce batch noise into the instance PET image by calculating statistics on batch level or introduce background noise into every single pixel by sharing the same learnable parameters spatially. In this paper, we proposed an attentive transformation (AT)-based normalization method for PET tumor segmentation. We exploit the distinguishability of breast tumor in PET images and dynamically generate dedicated and pixel-dependent learnable parameters in normalization via the transformation on a combination of channel-wise and spatial-wise attentive responses. The attentive learnable parameters allow to re-calibrate features pixel-by-pixel to focus on the high-uptake area while attenuating the background noise of PET images. Our experimental results on two real clinical datasets show that the AT-based normalization method improves breast tumor segmentation performance when compared with the existing normalization methods. [ABSTRACT FROM AUTHOR]

On-chip integration of nonreciprocal photonic devices is considered a “fundamental challenge” and the “missing link” for silicon photonics. Hybrid integration of magneto-optical (MO) materials in silicon photonic integrated circuits (PICs) shows promising potential to solve this challenge. In the past decade, the performance of silicon-integrated isolators and circulators based on hybrid integrated MO thin films has been significantly improved in terms of low insertion loss, high isolation ratio, and compact footprint. Accordingly, novel integrated magneto-photonic devices have emerged, including nonvolatile MO switch/memory, on-chip magnetometers, quantum nonreciprocal photonic devices, and topologically nontrivial lasers. In this paper, we review recent progress of on-chip nonreciprocal photonic devices based on hybrid integration of MO thin films on silicon. The configuration, operation principle, and key metrics of different types of integrated MO nonreciprocal photonic devices are summarized. In addition, the progress of on-chip electromagnets and novel MO devices are discussed. [ABSTRACT FROM AUTHOR]

This study proposes an experimental implementation of temperature sensing system to study the enhanced Vernier effect using two polydimethylsiloxane (PDMS)-cavity Fabry-Perot interferometers (FPIs) arranged in parallel. However, these devices exhibited opposite responses to temperature; that is, with an increase (or decrease) in temperature, the two FPIs shifted in red and blue wavelength directions, respectively. This indicated that the sensitivity amplification of their parallel configuration was considerably larger than that generated by the traditional Vernier effect. Experimental results showed that the temperature sensitivities of the two FPIs were −0.42 and 1.36 nm/°C while that of their parallel structure was −14.6 nm/°C corresponding to an amplification factor of 10.7, which was much larger than that (8.2) of the traditional Vernier effect. [ABSTRACT FROM AUTHOR]

Objective: The increasing demand for unraveling cellular heterogeneity has boosted single cell metabolomics studies. However, current analytical methods are usually labor-intensive and hampered by lack of accuracy and efficiency. Methods: we developed a first-ever automated single cell mass spectrometry system (named SCMS) that facilitated the metabolic profiling of single cells. In particular, extremely small droplets of sub nano-liter were generated to extract the single cells, and the underlying mechanism was verified theoretically and experimentally. This was crucial to minimize the dilution of the trace cellular contents and enhance the analytical sensitivity. Based on the precise 3D positioning of the pipette tip, we established a visual servoing robotic micromanipulation platform on which single cells were sequentially extracted, aspirated, and ionized, followed by the mass spectrometry analyses. Results: With the SCMS system, inter-operator variability was eliminated and working efficiency was improved. The performance of the SCMS system was validated by the experiments on bladder cancer cells. MS and MS2 analyses of single cells enable us to identify several cellular metabolites and the underlying inter-cell heterogeneity. Conclusion: In contrast to traditional methods, the SCMS system functions without human intervention and realizes a robust single cell metabolic analysis. Significance: the SCMS system upgrades the way how single cell metabolites were analyzed, and has the potential to be a powerful tool for single cell metabolomics studies. [ABSTRACT FROM AUTHOR]

Multimodal positron emission tomography-computed tomography (PET-CT) is used routinely in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection of PET and anatomical information from CT. Tumor segmentation is a critical element of PET-CT but at present, the performance of existing automated methods for this challenging task is low. Segmentation tends to be done manually by different imaging experts, which is labor-intensive and prone to errors and inconsistency. Previous automated segmentation methods largely focused on fusing information that is extracted separately from the PET and CT modalities, with the underlying assumption that each modality contains complementary information. However, these methods do not fully exploit the high PET tumor sensitivity that can guide the segmentation. We introduce a deep learning-based framework in multimodal PET-CT segmentation with a multimodal spatial attention module (MSAM). The MSAM automatically learns to emphasize regions (spatial areas) related to tumors and suppress normal regions with physiologic high-uptake from the PET input. The resulting spatial attention maps are subsequently employed to target a convolutional neural network (CNN) backbone for segmentation of areas with higher tumor likelihood from the CT image. Our experimental results on two clinical PET-CT datasets of non-small cell lung cancer (NSCLC) and soft tissue sarcoma (STS) validate the effectiveness of our framework in these different cancer types. We show that our MSAM, with a conventional U-Net backbone, surpasses the state-of-the-art lung tumor segmentation approach by a margin of 7.6% in Dice similarity coefficient (DSC). [ABSTRACT FROM AUTHOR]

In this letter, ferroelectric memory performance of TiN/Y-doped-HfO2 (HYO)/TiN capacitors is investigated under proton radiation with 3-MeV energy and different fluence (5e13, 1e14, 5e14, and 1e15 ions/cm2). X-ray diffraction patterns confirm that the orthorhombic phase Pbc21 of HYOfilm has no obvious change after proton radiation. Electrical characterization results demonstrate slight variations of the permittivity and ferroelectric hysteresis loop after proton radiation. The remanent polarization (2Pr) of the capacitor decreases with increasing proton fluence. But the decreasing trend of 2Pr is suppressed under high electric fields. Furthermore, the 2Pr degradation with cycling is abated by proton radiation. These results show that the HYO-based ferroelectric memory is highly resistive to proton radiation, which is potentially useful for space applications. [ABSTRACT FROM PUBLISHER]

Objective: Segmentation of skin lesions is an important step in the automated computer aided diagnosis of melanoma. However, existing segmentation methods have a tendency to over- or under-segment the lesions and perform poorly when the lesions have fuzzy boundaries, low contrast with the background, inhomogeneous textures, or contain artifacts. Furthermore, the performance of these methods are heavily reliant on the appropriate tuning of a large number of parameters as well as the use of effective preprocessing techniques, such as illumination correction and hair removal. Methods: We propose to leverage fully convolutional networks (FCNs) to automatically segment the skin lesions. FCNs are a neural network architecture that achieves object detection by hierarchically combining low-level appearance information with high-level semantic information. We address the issue of FCN producing coarse segmentation boundaries for challenging skin lesions (e.g., those with fuzzy boundaries and/or low difference in the textures between the foreground and the background) through a multistage segmentation approach in which multiple FCNs learn complementary visual characteristics of different skin lesions; early stage FCNs learn coarse appearance and localization information while late-stage FCNs learn the subtle characteristics of the lesion boundaries. We also introduce a new parallel integration method to combine the complementary information derived from individual segmentation stages to achieve a final segmentation result that has accurate localization and well-defined lesion boundaries, even for the most challenging skin lesions. Results: We achieved an average Dice coefficient of 91.18% on the ISBI 2016 Skin Lesion Challenge dataset and 90.66% on the PH2 dataset. Conclusion and Significance: Our extensive experimental results on two well-established public benchmark datasets demonstrate that our method is more effective than other state-of-the-art methods for skin lesion segmentation. [ABSTRACT FROM AUTHOR]

We study the Imbert–Fedorov (IF) effect in prism coupling configuration with magneto-optical (MO) material, which is called MOIF effect. An effective refractive index method MO waveguide is obtained based on the solution of Maxwell's equations. The expressions for IF and MOIF shifts are also derived. As left-handed circularly polarized (LHCP) and right-handed circularly polarized (RHCP) light beams have different effective refractive indices in MO waveguide when polar or longitudinal MO Kerr effects are taken into account, their IF shifts are asymmetric which are different from symmetric IF shifts in isotropic waveguides. By applying opposite magnetic field, a maximum MOIF shift close to 400 μm can be obtained with special waveguide configuration. MO effect will provide a new method to enhance and modulate the transverse shifts of LHCP and RHCP lights, which has not been considered up to now. [ABSTRACT FROM PUBLISHER]

In this letter, HfO2-based ferroelectric random access memory (FeRAM) with metal–insulator–metal structure is studied for the first time under radiation conditions. Y-doped HfO2-based FeRAM devices show high immunity to 60Co $\gamma $ ray radiation. Basic FeRAM parameters, such as leakage current, permittivity, remanent polarization, endurance, and fatigue, show almost no degradation after $\gamma $ ray radiation with a total dose as high as 12.96 Mrad (SI). Furthermore, the ferroelectric hysteresis loops show no distortion after radiation. The high stability of Y-doped HfO2 FeRAM devices under radiation demonstrates their great potential for nuclear and aerospace applications. [ABSTRACT FROM PUBLISHER]

Growth of phase pure yttrium iron garnet (YIG) on silicon is a challenging task due to the complexity of the Y–Fe–O phase diagram, and the lattice mismatch between Si and YIG. In this paper, we report the effect of deposition and post-deposition annealing (PDA) temperatures on the structure and the magnetic properties of YIG deposited on silicon using pulsed laser deposition. Both temperatures are found to be important for growing phase pure YIG on silicon. Non-garnet phases or mixed Fe valence states are observed in films when deposited or PDA at too high temperatures, whereas partially amorphous thin films are obtained when deposited or PDA at too low temperatures. YIG thin films with bulk-like structure and saturation magnetization are obtained for films deposited at 400 °C and PDA at 800 °C–850 °C, respectively. [ABSTRACT FROM AUTHOR]