Domain-specific optimization and diverse evaluation of self-supervised models for histopathology

Understand

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).