1 citations · 2 across the 9 of their papers we have counts for
5 papers · 1 filter
UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation
Yunzhou Li, Jiesi Hu, Yanwu Yang +5
Medical image segmentation foundation models are expected to generalize across diverse clinical scenarios, yet existing universal methods remain fragmented by prompt paradigms and…
Controllable Histopathology Image Synthesis with Training-free Structural Initialization and Textural Modulation
Yuheng Qiu, Jingyi Luo, Chenfei Ye +2
Deep learning has demonstrated remarkable success in high-throughput histopathology image analysis. However, the performance of learning-based models critically depends on the qual…
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…
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…
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…