UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation
Medical image segmentation foundation models are expected to generalize across diverse clinical scenarios, yet existing universal methods remain fragmented by prompt paradigms and spatial dimensions. Visual in-context learning, interactive segmentation, and language-guided segmentation are typically handled by paradigm-specific models, while 2D and 3D images are also modeled separately. Such isolation prevents heterogeneous annotations and data from being jointly absorbed by a single scalable model and limits cross-paradigm knowledge transfer. To address this bottleneck, we propose UniMedSeg,
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 49%DIAGNijmegen/rse-grand-challenge →
- PossiblePossibly related (embedding) · 49%voxelmorph/voxelmorph →
- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D” ≈ “Tongyi-MAI/Z-Image-Turbo””
- LinkedLinked via arxiv author · 85%Yunzhou Li →
“UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation”
- LinkedLinked via arxiv author · 85%Jiesi Hu →
“UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation”
- LinkedLinked via arxiv author · 85%Yanwu Yang →
“UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation”
- LinkedLinked via arxiv author · 85%Hanyang Peng →
“UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation”
- LinkedLinked via arxiv author · 85%Chenfei Ye →
“UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation”