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Pretraining a deep learning model on large image datasets is a standard stepbefore fine-tuning the model on small targeted datasets. The large dataset isusually general images (e.g. imagenet2012) while the small dataset can bespecialized datasets that have different distributions from the large dataset.However, this 'large-to-small' strategy is not well-validated when the largedataset is specialized and has a similar distribution to small datasets. Wenewly compiled three hematoxylin and eosin-stained image datasets, one large(PTCGA200) and two magnification-adjusted small datasets (PCam200 andsegPANDA200). Major deep learning models were trained with supervised andself-supervised learning methods and fine-tuned on the small datasets for tumorclassification and tissue segmentation benchmarks. ResNet50 pretrained withMoCov2, SimCLR, and BYOL on PTCGA200 was better than imagenet2012 pretrainingwhen fine-tuned on PTCGA200 (accuracy of 83.94%, 86.41%, 84.91%, and 82.72%,respectively). ResNet50 pre-trained on PTCGA200 with MoCov2 exceeded theCOCOtrain2017-pretrained baseline and was the best in ResNet50 for the tissuesegmentation benchmark (mIoU of 63.53% and 63.22%). We found re-trainingimagenet-pretrained models (ResNet50, BiT-M-R50x1, and ViT-S/16) on PTCGA200improved downstream benchmarks.

Large-scale pretraining on pathological images for fine-tuning of small pathological benchmarks