Domain-specific optimization and diverse evaluation of self-supervised
  models for histopathology
Jeremy Lai Faruk Ahmed Supriya Vijay Tiam Jaroensri Jessica Loo Saurabh Vyawahare Saloni Agarwal Fayaz Jamil Yossi Matias Greg S. Corrado Dale R. Webster Jonathan Krause Yun Liu Po-Hsuan Cameron Chen Ellery Wulczyn David F. Steiner
Task-specific deep learning models in histopathology offer promising
opportunities for improving diagnosis, clinical research, and precision
medicine. However, development of such models is often limited by availability
of high-quality data. Foundation models in histopathology that learn general
representations across a wide range of tissue types, diagnoses, and
magnifications offer the potential to reduce the data, compute, and technical
expertise necessary to develop task-specific deep learning models with the
required level of model performance. In this work, we describe the development
and evaluation of foundation models for histopathology via self-supervised
learning (SSL). We first establish a diverse set of benchmark tasks involving
17 unique tissue types and 12 unique cancer types and spanning different
optimal magnifications and task types. Next, we use this benchmark to explore
and evaluate histopathology-specific SSL methods followed by further evaluation
on held out patch-level and weakly supervised tasks. We found that standard SSL
methods thoughtfully applied to histopathology images are performant across our
benchmark tasks and that domain-specific methodological improvements can
further increase performance. Our findings reinforce the value of using
domain-specific SSL methods in pathology, and establish a set of high quality
foundation models to enable further research across diverse applications.