2023/10/10 by Gabriele Campanella, Ricky Kwan, Campanella, Gabriele +23 · 15 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #AI in cancer detection #Artificial Intelligence (cs.AI) #Artificial intelligence #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Deep learning #Digital pathology #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Machine learning #Radiomics and Machine Learning in Medical Imaging #Scale (ratio) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2310.07033
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/10/10 · openalex created_date 2023/10/13 · openalex updated_date 2026/07/28
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.