61 citations · 101 across the 5 of their papers we have counts for
5 papers
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…
Edge-aware Feature Aggregation Network for Polyp Segmentation
Tao Zhou, Yizhe Zhang, Geng Chen +3
Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer (CRC) in clinical practice. However, due to scale variation and blurry polyp boundar…
SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation
Yizhe Zhang, Tao Zhou, Shuo Wang +3
The Segment Anything Model (SAM) exhibits a capability to segment a wide array of objects in natural images, serving as a versatile perceptual tool for various downstream image seg…
Can SAM Segment Polyps?
Tao Zhou, Yizhe Zhang, Yi Zhou +2
Recently, Meta AI Research releases a general Segment Anything Model (SAM), which has demonstrated promising performance in several segmentation tasks. As we know, polyp segmentati…