CytoFormer: A Molecularly Supervised Cell Foundation Model for Histopathology Cell Classification
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TL;DR - CytoFormer is a cell foundation model trained on 15.4 million paired H&E image patches and spatial-transcriptomics-derived labels. It enables accurate, label-efficient cell classification across organs without relying on large-scale manual pathology annotation.
- Covers 23 cell types across 81 tissue sections from 16 organs.
- Achieved 0.85 accuracy and 0.78 macro-F1 on spatially held-out tissue.
- Frozen CytoFormer features outperformed six pathology foundation models on four expert-annotated transfer benchmarks.
- In active learning, it identified normal epithelium among tumor look-alikes with 0.82 F1 using only a few annotations, exceeding the strongest baseline by 0.13.
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CytoFormer: A Molecularly Supervised Cell Foundation Model for Histopathology Cell Classification
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TL;DR - CytoFormer is a cell foundation model trained on 15.4 million paired H&E image patches and spatial-transcriptomics-derived labels. It enables accurate, label-efficient cell classification across organs without relying on large-scale manual pathology annotation.
- Covers 23 cell types across 81 tissue sections from 16 organs.
- Achieved 0.85 accuracy and 0.78 macro-F1 on spatially held-out tissue.
- Frozen CytoFormer features outperformed six pathology foundation models on four expert-annotated transfer benchmarks.
- In active learning, it identified normal epithelium among tumor look-alikes with 0.82 F1 using only a few annotations, exceeding the strongest baseline by 0.13.