IEEE

Certainty-Guided Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation

Abstract: Semi-supervised learning (SSL) enables the accurate segmentation of medical images with limited available labeled data. However, its performance usually lags fully supervised methods that ...
IEEE

Multimodal Supervised Contrastive Learning in Remote Sensing Downstream Tasks

Abstract: To leverage the large amount of unlabeled data available in remote sensing datasets, self-supervised learning (SSL) methods have recently emerged as an ubiquitous tool to pretrain robust ...
GitHub

Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection

PyTorch implementation for CVPR2023 paper, Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection. python main.py --flow_arch ...
GitHub

Hunting Sparsity: Density-Guided Contrastive Learning for Semi-Supervised Semantic Segmentation

Xiaoyang Wang, Bingfeng Zhang, Limin Yu, and Jimin Xiao. In CVPR 2023. Abstract: Inspired by density-based unsupervised clustering, we propose to leverage feature density to locate sparse regions ...
Journal of Medical Internet Research

Disease Concept-Embedding Based on the Self-Supervised Method for Medical Information Extraction from Electronic Health Records and Disease Retrieval: Algorithm Development and ...

Conclusions: Through contrastive learning methods, disease concepts can be embedded meaningfully. Moreover, these methods can be used for disease retrieval tasks to enhance clinical practice ...