Towards Better Dermoscopic Image Feature Representation Learning for Melanoma Classification

Deep learning-based melanoma classification with dermoscopic images has recently shown great potential in automatic early-stage melanoma diagnosis. However, limited by the significant data imbalance and obvious extraneous artifacts, i.e., the hair and ruler markings, discriminative feature extraction from dermoscopic images is very challenging. In this study, we seek to resolve these problems respectively towards better representation learning for lesion features. Specifically, a GAN-based data augmentation (GDA) strategy is adapted to generate synthetic melanoma-positive images, in conjunction with the proposed implicit hair denoising (IHD) strategy. Wherein the hair-related representations are implicitly disentangled via an auxiliary classifier network and reversely sent to the melanoma-feature extraction backbone for better melanoma-specific representation learning. Furthermore, to train the IHD module, the hair noises are additionally labeled on the ISIC2020 dataset, making it the first large-scale dermoscopic dataset with annotation of hair-like artifacts. Extensive experiments demonstrate the superiority of the proposed framework as well as the effectiveness of each component. The improved dataset publicly avaliable at https://github.com/kirtsy/DermoscopicDataset.Comment: ICONIP 2021 conferenc

A Novel Faulty Phase Selection Method for Single-Phase-to-Ground Fault in Distribution System Based on Transient Current Similarity Measurement

In modern electrical power distribution systems, the effective operation of inverter-based arc suppression devices relies on the accuracy of faulty phase selection. In the traditional methods of faulty phase selection for single-phase-to-ground faults (SPGs), power frequency-based amplitude and phase characteristics are used to identify the faulty phase. In the field, when a high-resistance SPG occurs in the system, traditional methods are difficult for accurately identifying the faulty phase because of the weak fault components and complicated process. A novel realizable and effective method of faulty phase selection based on transient current similarity measurements is presented when SPGs occur in resonantly grounded distribution systems in this paper. An optimized Hausdorff distance matrix (MOHD) is proposed and constructed by the transient currents of three phases’ similarity measurements within a certain time window of our method. This MOHD is used to select the sampling time window adaptively, which allows the proposed method to be applied to any scale of distribution systems. Firstly, when a SPG occurs, the expressions for the transient phase current mutation in the faulty and sound phases are analyzed. Then, the sampling process is segmented into several selection units (SUs) to form the MOHD-based faulty phase selection method. Additionally, the Hausdorff distance algorithm (HD) is used to calculate the waveform similarities of the transient phase current mutation among the three phases to form the HD-based faulty phase selection method. Finally, a practical resonant grounded distribution system is modeled in PSCAD/EMTDC, and the effectiveness and performance of the proposed method is compared and verified under different fault resistances, fault inception angles, system topologies, sampling time windows and rates of data missing

A Medical Semantic-Assisted Transformer for Radiographic Report Generation

Automated radiographic report generation is a challenging cross-domain task that aims to automatically generate accurate and semantic-coherence reports to describe medical images. Despite the recent progress in this field, there are still many challenges at least in the following aspects. First, radiographic images are very similar to each other, and thus it is difficult to capture the fine-grained visual differences using CNN as the visual feature extractor like many existing methods. Further, semantic information has been widely applied to boost the performance of generation tasks (e.g. image captioning), but existing methods often fail to provide effective medical semantic features. Toward solving those problems, in this paper, we propose a memory-augmented sparse attention block utilizing bilinear pooling to capture the higher-order interactions between the input fine-grained image features while producing sparse attention. Moreover, we introduce a novel Medical Concepts Generation Network (MCGN) to predict fine-grained semantic concepts and incorporate them into the report generation process as guidance. Our proposed method shows promising performance on the recently released largest benchmark MIMIC-CXR. It outperforms multiple state-of-the-art methods in image captioning and medical report generation

Supplemental Material - De Ritis Ratio is Associated with Contrast-Associated Acute Kidney Injury Prediction and Long-Term Clinical Outcomes in Patients Undergoing Emergency Percutaneous Coronary Intervention

Supplemental Material for De Ritis Ratio is Associated with Contrast-Associated Acute Kidney Injury Prediction and Long-Term Clinical Outcomes in Patients Undergoing Emergency Percutaneous Coronary Intervention by Wenkang Zhang, Mingkang Li, Xu Huang, Minhao Zhang, Gaoliang Yan, and Chengchun Tang in Angiology</p