8 citations · 8 across the 2 of their papers we have counts for
4 papers
Active Source-free Domain Adaptation in Open-set Medical Image Segmentation via Decomposed Uncertainty and Prototype Discrepancy
Jin Yang, Yichi Zhang, Peijie Qiu +1
Deep learning (DL) methods are challenged to demonstrate robust performance across different segmentation datasets due to domain shifts, but active domain adaptation techniques enh…
Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions
Yichi Zhang, Zhenrong Shen, Rushi Jiao
Due to the inherent flexibility of prompting, foundation models have emerged as the predominant force in the fields of natural language processing and computer vision. The recent i…
SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization
Yichi Zhang, Jin Yang, Yuchen Liu +2
Semi-supervised learning has attracted much attention due to its less dependence on acquiring abundant annotations from experts compared to fully supervised methods, which is espec…
Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification
Yichi Zhang, Shiyao Hu, Sijie Ren +3
The Segment Anything Model (SAM) has recently emerged as a groundbreaking foundation model for prompt-driven image segmentation tasks. However, both the original SAM and its medica…