Kaiko midnight Midnight Training State of the Art Pathology Foundation Models with Orders of Magnitude Less Data This repository contains the model checkpoints for the Midnight 12k model presented in our paper titled "Training state of the art pathology foundation models with orders of magnitude less data." Our approach achieves competitive performance compared to leading pathology foundation models (FMs), despite being trained on significantly fewer whole slide images (WSIs). Overview We propose a refined self supervised training framework based on DINOv2 with modifications that optimize model performance specifically for computational pathology. Our main contributions include: Three novel pathology FMs trained with significantly reduced data (up to 100x fewer WSIs). Introduction of high resolution post training to enhance embedding quality. Model Highlights Midnight 12k : Trained exclusively on the publicly available TCGA dataset (12k WSIs). Midnight 92k : Trained on TCGA and an additional proprietary dataset from the Netherlands Cancer Institute (NKI 80k). Midnight 92k/392 : Our top performing model fine tuned with high resolution post training. Model Weights Midnight 12k: Publi…
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