Histopathology Representation Learning

Histopathology representation learning aims to develop computational methods that effectively capture the complex visual information within digital pathology images, enabling improved diagnostic accuracy and efficiency. Current research focuses on leveraging deep learning architectures, including transformers and autoencoders, often employing self-supervised learning techniques to overcome limitations of scarce labeled data and address challenges like computational cost and class imbalance. These advancements hold significant promise for improving the objectivity and speed of histopathological diagnoses, potentially leading to better patient care and accelerating research in disease understanding.