36 citations · 71 across the 10 of their papers we have counts for
5 papers · 1 filter
Information-guided pixel augmentation for pixel-wise contrastive learning
Quan Quan, Qingsong Yao, Jun Li +1
Contrastive learning (CL) is a form of self-supervised learning and has been widely used for various tasks. Different from widely studied instance-level contrastive learning, pixel…
Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Review
Zikang Xu, Jun Li, Qingsong Yao +3
Deep learning algorithms have demonstrated remarkable efficacy in various medical image analysis (MedIA) applications. However, recent research highlights a performance disparity i…
Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Jun Li, Junyu Chen, Yucheng Tang +3
Transformer, the latest technological advance of deep learning, has gained prevalence in natural language processing or computer vision. Since medical imaging bear some resemblance…
MixCL: Pixel label matters to contrastive learning
Jun Li, Quan Quan, S. Kevin Zhou
Contrastive learning and self-supervised techniques have gained prevalence in computer vision for the past few years. It is essential for medical image analysis, which is often not…
Universal Segmentation of 33 Anatomies
Pengbo Liu, Yang Deng, Ce Wang +12
In the paper, we present an approach for learning a single model that universally segments 33 anatomical structures, including vertebrae, pelvic bones, and abdominal organs. Our mo…