4 papers
Memory-Supported Synergistic Adaptation for Training-Free Test-Time Medical Image Segmentation
Lingrui Li, Nan Pu, Dong Zhao +4
Test-time adaptation (TTA) aims to mitigate distribution shifts by adapting models with unlabeled target data at inference time. While TTA with vision-language models (VLMs) has sh…
Multi-Scale Global-Instance Prompt Tuning for Continual Test-time Adaptation in Medical Image Segmentation
Lingrui Li, Yanfeng Zhou, Nan Pu +2
Distribution shift is a common challenge in medical images obtained from different clinical centers, significantly hindering the deployment of pre-trained semantic segmentation mod…
XNet v2: Fewer Limitations, Better Results and Greater Universality
Yanfeng Zhou, Lingrui Li, Zichen Wang +3
XNet introduces a wavelet-based X-shaped unified architecture for fully- and semi-supervised biomedical segmentation. So far, however, XNet still faces the limitations, including p…
Robust Source-Free Domain Adaptation for Fundus Image Segmentation
Lingrui Li, Yanfeng Zhou, Ge Yang
Unsupervised Domain Adaptation (UDA) is a learning technique that transfers knowledge learned in the source domain from labelled training data to the target domain with only unlabe…