Organ-Agnostic Whole Slide Image Analysis using Self-Supervised Transfer Learning

Traditional methods for pathology image analysis are well-known for their time-consuming and labor-intensive nature, often relying on the expertise of pathologists. In recent years, numerous research studies have been proposed to develop automated systems using machine learning approaches to address these challenges. While these systems have demonstrated promising performance, they often exhibit bias towards specific organs, cells, or tasks, limiting their ability to provide generalized solutions for pathology image analysis. To address this issue, I propose an organ-agnostic pathology image analysis system that leverages a self-supervised transfer learning approach. The proposed system comprises two stages: self-supervised representation learning and transfer learning. In the self-supervised representation learning phase, a machine learning model is trained to consistently extract essential features encapsulating the characteristics of diverse pathological images such as visual patterns of tumors. Subsequently, in the transfer learning phase, these well-pretrained models are utilized to train downstream tasks, such as tumor type classification or cancer area segmentation. The proposed approach outperforms all existing state-of-the-art supervised methods in multiple public pathology image benchmarks.