DC-Seg trains a single model that aligns anatomical features across MRI modalities with contrastive learning and adds a per-modality segmentation regularizer, yielding higher Dice scores when modalities are missing.
In: Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13
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DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing Modalities
DC-Seg trains a single model that aligns anatomical features across MRI modalities with contrastive learning and adds a per-modality segmentation regularizer, yielding higher Dice scores when modalities are missing.