Segmentation of Pericardial Adipose Tissue in CMR Images: A Benchmark Dataset MRPEAT and a Triple-Stage Network 3SUnet.

Increased pericardial adipose tissue (PEAT) is associated with a series of cardiovascular diseases (CVDs) and metabolic syndromes. Quantitative analysis of PEAT by means of image segmentation is of great significance. Although cardiovascular magnetic resonance (CMR) has been utilized as a routine method for non-invasive and non-radioactive CVD diagnosis, segmentation of PEAT in CMR images is challenging and laborious.

Knowledge-Preserving continual person re-identification using Graph Attention Network.

Person re-identification (ReID), considered as a sub-problem of image retrieval, is critical for intelligent security. The general practice is to train a deep model on images from a particular scenario (also known as a domain) and perform retrieval tests on images from the same domain. Thus, the model has to be retrained to ensure good performance on unseen domains.

GSEnet: feature extraction of gene expression data and its application to Leukemia classification.

Gene expression data is highly dimensional. As disease-related genes account for only a tiny fraction, a deep learning model, namely GSEnet, is proposed to extract instructive features from gene expression data. This model consists of three modules, namely the pre-conv module, the SE-Resnet module, and the SE-conv module.