4 papers
Can General-Purpose Omnimodels Compete with Specialists? A Case Study in Medical Image Segmentation
Yizhe Zhang, Qiang Chen, Tao Zhou
The emergence of powerful, general-purpose omnimodels capable of processing diverse data modalities has raised a critical question: can these ``jack-of-all-trades'' systems perform…
Uncertainty-aware Cross-training for Semi-supervised Medical Image Segmentation
Kaiwen Huang, Tao Zhou, Huazhu Fu +3
Semi-supervised learning has gained considerable popularity in medical image segmentation tasks due to its capability to reduce reliance on expert-examined annotations. Several mea…
Continual Retinal Vision-Language Pre-training upon Incremental Imaging Modalities
Yuang Yao, Ruiqi Wu, Yi Zhou +1
Traditional fundus image analysis models focus on single-modal tasks, ignoring fundus modality complementarity, which limits their versatility. Recently, retinal foundation models…
Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation
Kaiwen Huang, Tao Zhou, Huazhu Fu +4
The limited availability of labeled data has driven advancements in semi-supervised learning for medical image segmentation. Modern large-scale models tailored for general segmenta…