oai:arXiv.org:2310.07033
Computer Science
2023
10/18/2023
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.
We collected the largest pathology dataset to date, consisting of over 3 billion images from over 423 thousand microscopy slides.
We compared pre-training of visual transformer models using the masked autoencoder (MAE) and DINO algorithms.
We evaluated performance on six clinically relevant tasks from three anatomic sites and two institutions: breast cancer detection, inflammatory bowel disease detection, breast cancer estrogen receptor prediction, lung adenocarcinoma EGFR mutation prediction, and lung cancer immunotherapy response prediction.
Our results demonstrate that pre-training on pathology data is beneficial for downstream performance compared to pre-training on natural images.
Additionally, the DINO algorithm achieved better generalization performance across all tasks tested.
The presented results signify a phase change in computational pathology research, paving the way into a new era of more performant models based on large-scale, parallel pre-training at the billion-image scale.
Campanella, Gabriele,Kwan, Ricky,Fluder, Eugene,Zeng, Jennifer,Stock, Aryeh,Veremis, Brandon,Polydorides, Alexandros D.,Hedvat, Cyrus,Schoenfeld, Adam,Vanderbilt, Chad,Kovatch, Patricia,Cordon-Cardo, Carlos,Fuchs, Thomas J., 2023, Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Images