MM-DINOv2, which adds modality embeddings, per-modality positional embeddings, full-modality masking, and semi-supervised training to DINOv2, improved external-test glioma subtype classification to MCC 0.60 versus 0.54 for ResNet34.
Scientific Data4(1), 170117 (sep 2017)
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MM-DINOv2: Adapting Foundation Models for Multi-Modal Medical Image Analysis
MM-DINOv2, which adds modality embeddings, per-modality positional embeddings, full-modality masking, and semi-supervised training to DINOv2, improved external-test glioma subtype classification to MCC 0.60 versus 0.54 for ResNet34.