Formalizes continual segmentation under coupled class-domain-label shifts and introduces gradient-adaptive stabilization plus prototype consistency for semi-supervised learning in heterogeneous dense prediction.
URL https:// repo-prod.prod.sagebase.org/repo/v1/ doi/locate?id=syn3193805&type=ENTITY
4 Pith papers cite this work, alongside 221 external citations. Polarity classification is still indexing.
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Semantic-aware random convolution and intensity-based source matching enable effective single-source domain generalization for medical image segmentation, outperforming prior methods and sometimes matching in-domain performance.
OBBSeg segments irregular medical lesions from oriented bounding-box labels via a Mask-to-OBB loss and prompt modules, claiming near fully-supervised accuracy across 13 datasets and 5 modalities.
MedCAGD introduces a context-aware gated decoder with channel recalibration, gated skip fusion, and global context aggregation that outperforms baselines on 11 medical segmentation benchmarks while remaining computationally practical.
citing papers explorer
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Continual Segmentation under Joint Nonstationarity
Formalizes continual segmentation under coupled class-domain-label shifts and introduces gradient-adaptive stabilization plus prototype consistency for semi-supervised learning in heterogeneous dense prediction.
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Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation
Semantic-aware random convolution and intensity-based source matching enable effective single-source domain generalization for medical image segmentation, outperforming prior methods and sometimes matching in-domain performance.
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OBBSeg: Irregular Lesion Segmentation under Oriented Bounding Box Annotations
OBBSeg segments irregular medical lesions from oriented bounding-box labels via a Mask-to-OBB loss and prompt modules, claiming near fully-supervised accuracy across 13 datasets and 5 modalities.
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MedCAGD: Context-Aware Gated Decoder for Efficient Medical Image Segmentation
MedCAGD introduces a context-aware gated decoder with channel recalibration, gated skip fusion, and global context aggregation that outperforms baselines on 11 medical segmentation benchmarks while remaining computationally practical.