3 papers
cs.CV2025
Efficient Universal Models for Medical Image Segmentation via Weakly Supervised In-Context Learning
Jiesi Hu, Yanwu Yang, Zhiyu Ye +4
Universal models for medical image segmentation, such as interactive and in-context learning (ICL) models, offer strong generalization but require extensive annotations. Interactiv…
cs.CV2025
Towards Robust In-Context Learning for Medical Image Segmentation via Data Synthesis
Jiesi Hu, Yanwu Yang, Zhiyu Ye +4
The rise of In-Context Learning (ICL) for universal medical image segmentation has introduced an unprecedented demand for large-scale, diverse datasets for training, exacerbating t…
cs.CV2025
Medverse: A Universal Model for Full-Resolution 3D Medical Image Segmentation, Transformation and Enhancement
Jiesi Hu, Jianfeng Cao, Yanwu Yang +4
In-context learning (ICL) offers a promising paradigm for universal medical image analysis, enabling models to perform diverse image processing tasks without retraining. However, c…