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.
Generalist medical foundation model improves prostate cancer segmentation from multimodal MRI images,
2 Pith papers cite this work, alongside 527 external citations. Polarity classification is still indexing.
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PaRC-mix applies coherent feature mixup across parallel routes in residual networks to reduce domain shift in prostate cancer segmentation from ADC MRI across five external scanner datasets.
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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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Enhancing Prostate Cancer Segmentation for Multi-Domain Generalization using a novel Parallel-Route Coherent Mixup Regularization Training
PaRC-mix applies coherent feature mixup across parallel routes in residual networks to reduce domain shift in prostate cancer segmentation from ADC MRI across five external scanner datasets.