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.
Radiology: Artificial Intelligence4(6), e220058 (nov 2022)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.IV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.