61 citations · 102 across the 21 of their papers we have counts for
5 papers · 1 filter
SQA-SAM: Segmentation Quality Assessment for Medical Images Utilizing the Segment Anything Model
Yizhe Zhang, Shuo Wang, Tao Zhou +2
Segmentation quality assessment (SQA) plays a critical role in the deployment of a medical image based AI system. Users need to be informed/alerted whenever an AI system generates…
Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation
Yunqi Gu, Tao Zhou, Yizhe Zhang +4
Medical image segmentation plays a crucial role in computer-aided diagnosis. However, existing methods heavily rely on fully supervised training, which requires a large amount of l…
Segment Anything Model-guided Collaborative Learning Network for Scribble-supervised Polyp Segmentation
Yiming Zhao, Tao Zhou, Yunqi Gu +4
Polyp segmentation plays a vital role in accurately locating polyps at an early stage, which holds significant clinical importance for the prevention of colorectal cancer. Various…
A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends
Jiaxin Mei, Tao Zhou, Kaiwen Huang +4
Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist…
Memory-Assisted Sub-Prototype Mining for Universal Domain Adaptation
Yuxiang Lai, Yi Zhou, Xinghong Liu +1
Universal domain adaptation aims to align the classes and reduce the feature gap between the same category of the source and target domains. The target private category is set as t…