Optimal Transport Metric Learning for Feature Alignment in Partially Supervised Segmentation
Multi-organ segmentation is often challenged by partially annotated datasets and domain shifts across different imaging sources. To address these limitations, we propose a two-stage learning framework that efficiently leverages partial supervision. In the first stage, the model learns from available annotations to produce accurate segmentations of annotated organs, establishing robust feature representations. In the second stage, we introduce learnable organ prototypes and a Sinkhorn-triplet loss to enforce organ-wise feature consistency across datasets. This encourages latent embeddings of the same organ to remain close, while increasing separation between different organs, even when ann...
| Authors | Dakini Mallam Garba, Salim Abdou Daoura |
|---|---|
| Primary category | eess.IV |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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