Machine learning methods for histopathological image analysis: Updates in 2024
Daisuke Komura, Mieko Ochi, Shumpei Ishikawa
Abstract
The combination of artificial intelligence and digital pathology has emerged as a transformative force in healthcare and biomedical research. As an update to our 2018 review, this review presents comprehensive analysis of machine learning applications in histopathological image analysis, with focus on the developments since 2018. We highlight significant advances that have expanded the technical capabilities and practical applications of computational pathology. The review examines progress in addressing key challenges in the field as follows: processing of gigapixel whole slide images, insufficient labeled data, multidimensional analysis, domain shifts across institutions, and interpretability of machine learning models. We evaluate emerging trends, such as foundation models and multimodal integration, that are reshaping the field. Overall, our review highlights the potential of machine learning in enhancing both routine pathological analysis and scientific discovery in pathology. By providing this comprehensive overview, this review aims to guide researchers and clinicians in understanding the current state of the pathology image analysis field and its future trajectory.