3 papers
cs.CV2026
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…
cs.CV2025
MO-CTranS: A unified multi-organ segmentation model learning from multiple heterogeneously labelled datasets
Zhendi Gong, Susan Francis, Eleanor Cox +5
Multi-organ segmentation holds paramount significance in many clinical tasks. In practice, compared to large fully annotated datasets, multiple small datasets are often more access…
cs.CV2025
An Attentive Representative Sample Selection Strategy Combined with Balanced Batch Training for Skin Lesion Segmentation
Stephen Lloyd-Brown, Susan Francis, Caroline Hoad +4
An often overlooked problem in medical image segmentation research is the effective selection of training subsets to annotate from a complete set of unlabelled data. Many studies s…