Histopathology Datasets

Histopathology datasets are collections of digitized tissue images used to train and evaluate computer vision models for automated disease diagnosis and prognosis. Current research focuses on addressing challenges like data bias, limited annotation, and domain generalization, employing various deep learning architectures including Vision Transformers, U-Nets, and ResNets, often combined with techniques like multiple instance learning and self-supervised learning. These advancements aim to improve the accuracy, robustness, and explainability of computational pathology tools, ultimately assisting pathologists in making more efficient and informed diagnoses. The development of large, diverse, and well-curated datasets is crucial for realizing the full potential of AI in histopathology.