  To handle the large scale of whole slide images in computational pathology,
most approaches first tessellate the images into smaller patches, extract
features from these patches, and finally aggregate the feature vectors with
weakly-supervised learning. The performance of this workflow strongly depends
on the quality of the extracted features. Recently, foundation models in
computer vision showed that leveraging huge amounts of data through supervised
or self-supervised learning improves feature quality and generalizability for a
variety of tasks. In this study, we benchmark the most popular vision
foundation models as feature extractors for histopathology data. We evaluate
the models in two settings: slide-level classification and patch-level
classification. We show that foundation models are a strong baseline. Our
experiments demonstrate that by finetuning a foundation model on a single GPU
for only two hours or three days depending on the dataset, we can match or
outperform state-of-the-art feature extractors for computational pathology.
These findings imply that even with little resources one can finetune a feature
extractor tailored towards a specific downstream task and dataset. This is a
considerable shift from the current state, where only few institutions with
large amounts of resources and datasets are able to train a feature extractor.
We publish all code used for training and evaluation as well as the finetuned
models.
