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. In practice, no public CMR datasets are available for validating PEAT automatic segmentation. Therefore, we first release a benchmark CMR dataset, MRPEAT, which consists of cardiac short axis (SA) CMR images from 50 hypertrophic cardiomyopathy (HCM), 50 acute myocardial infarction (AMI), and 50 normal control (NC) subjects. We then propose a deep learning model, named as 3SUnet, to segment PEAT on MRPEAT to tackle the challenges that PEAT is relatively small and diverse and its intensities are hard to distinguish from the background. The 3SUnet is a triple-stage network, of which the backbones are all Unet. One Unet is used to extract a region of interest (ROI) for any given image with ventricles and PEAT being contained completely using a multi-task continual learning strategy. Another Unet is adopted to segment PEAT in ROI-cropped images. The third Unet is utilized to refine PEAT segmentation accuracy guided by an image adaptive probability map. The proposed model is qualitatively and quantitatively compared with the state-of-the-art models on the dataset. We obtain the PEAT segmentation results through 3SUnet, assess the robustness of 3SUnet under different pathological conditions, and identify the imaging indications of PEAT in CVDs. The dataset and all source codes are available at https://dflag-neu.github.io/member/csz/research/.

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. Unfortunately, retraining will introduce the so called catastrophic forgetting problem existing in deep learning models. To address this problem, we propose a Continual person re-identification model via a Knowledge-Preserving (CKP) mechanism. The proposed model is able to accumulate knowledge from continuously changing scenarios. The knowledge is updated via a graph attention network from the human cognitive-inspired perspective as the scenario changes. The accumulated knowledge is used to guide the learning process of the proposed model on image samples from new-coming domains. We finally evaluate and compare CKP with fine-tuning, continual learning in image classification and person re-identification, and joint training. Experiments on representative benchmark datasets (Market1501, DukeMTMC, CUHK03, CUHK-SYSU, and MSMT17, which arrive in different orders) demonstrate the advantages of the proposed model in preventing forgetting, and experiments on other benchmark datasets (GRID, SenseReID, CUHK01, CUHK02, VIPER, iLIDS, and PRID, which are not available during training) demonstrate the generalization ability of the proposed model. The CKP outperforms the best comparative model by 0.58% and 0.65% on seen domains (datasets available during training), and by 0.95% and 1.02% on never seen domains (datasets not available during training) in terms of mAP and Rank1, respectively. Arrival order of the training datasets, guidance of accumulated knowledge for learning new knowledge and parameter settings are also discussed.

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. Effectiveness of the proposed model on the performance improvement of 9 representative classifiers is evaluated. Seven evaluation metrics are used for this assessment on the GSE99095 dataset. Robustness and advantages of the proposed model compared with representative feature selection methods are also discussed. Results show superiority of the proposed model on the improvement of the classification precision and accuracy.