Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Images

Understand

Recent breakthroughs in self-supervised learning have enabled the use of large unlabeled datasets to train visual foundation models that can generalize to a variety of downstream tasks.

• While this training paradigm is well suited for the medical domain where annotations are scarce, large-scale pre-training in the medical domain, and in particular pathology, has not been extensively studied.
• Previous work in self-supervised learning in pathology has leveraged smaller datasets for both pre-training and evaluating downstream performance.
• The aim of this project is to train the largest academic foundation model and benchmark the most prominent self-supervised learning algorithms by pre-training and evaluating downstream performance on large clinical pathology datasets.