Fig. 2: UMAP embeddings of tibial artery tile features from three representation learning methods. | Nature Communications

Fig. 2: UMAP embeddings of tibial artery tile features from three representation learning methods.

From: Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types

Fig. 2

A ResNet50 with pretrained weights from ImageNet. B CTransPath (pretrained on 15 M histology images). C Our self-supervised ViT-S model trained using self-distillation with no labels (DINO). Tiles have been manually labelled with tissue substructures/pathologies to interpret clusters. DINO embeddings show both better qualitative clustering and quantitative silhouette scores, a 43% improvement over CTransPath.

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