arXiv Computer Vision
Unified CT and MRI Pancreas Segmentation for Label-Efficient Cross-Modality Subregion Transfer
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arXiv Computer Vision
Sep 10
Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalization
arXiv:2602.20773v2 Announce Type: replace Abstract: Purpose: Developing generalizable medical image segmentation models is challenging because imaging data are distributed across institutions and dif...
arXiv AI
Jul 16
Multimodal Assessment of Pancreatic Cancer Resectability Using Deep Learning
arXiv:2607. 13826v1 Announce Type: cross Abstract: Accurate determination of pancreatic ductal adenocarcinoma (PDAC) resectability relies on evaluating how the tumor interacts with major peripancreatic vessels on CT imaging, yet expert assessment often shows substantial variability.
arXiv Computer Vision
Oct 22
ReSeg: Retraining-free segmentation of abdominal organs via generalizable semantic representation learning
arXiv:2610.29090v1 Announce Type: new Abstract: Abdominal organ segmentation is essential in modern medical applications. While existing deep learning approaches demonstrate promising performance, th...
arXiv AI
Sep 18
Optimal Transport Metric Learning for Feature Alignment in Partially Supervised Segmentation
The paper proposes a two‑stage learning framework for multi‑organ segmentation that handles partially annotated datasets and domain shifts. First, the model learns accurate segmentations from available annotations to build robust feature representations. Second, it introduces learnable organ prototypes and a Sinkhorn‑triplet loss to enforce organ‑wise feature consistency across datasets, keeping embeddings of the same organ close while separating different organs, even when annotations are missing.
arXiv Computer Vision
Aug 25
SymmAdapt: Symmetrical Flow Matching for Source-Free Domain Adaptation in Medical Image Segmentation
arXiv:2608.22532v1 Announce Type: new Abstract: Domain shift across imaging modalities and acquisition sites remains a significant barrier to the clinical deployment of segmentation models. Source-fr...
arXiv Computer Vision
Oct 8
CIRSeg: Coarse-to-Fine Intensity-Robust Liver Segmentation with Source-Free Continual Test-Time Adaptation
arXiv:2610.09784v1 Announce Type: new Abstract: Reliable liver segmentation in contrast-enhanced MRI is essential for quantitative hepatic assessment, treatment planning, and longitudinal disease mon...