Komura Daisuke, Ochi Mieko, Ishikawa Shumpei
Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Comput Struct Biotechnol J. 2024 Dec 30;27:383-400. doi: 10.1016/j.csbj.2024.12.033. eCollection 2025.
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.
人工智能与数字病理学的结合已成为医疗保健和生物医学研究中的一股变革力量。作为对我们2018年综述的更新，本综述对机器学习在组织病理学图像分析中的应用进行了全面分析，重点关注2018年以来的发展。我们强调了显著进展，这些进展扩展了计算病理学的技术能力和实际应用。本综述考察了该领域在应对以下关键挑战方面的进展：千兆像素全切片图像的处理、标记数据不足、多维度分析、不同机构间的领域转移以及机器学习模型的可解释性。我们评估了正在重塑该领域的新兴趋势，如基础模型和多模态整合。总体而言，我们的综述突出了机器学习在加强常规病理分析和病理学科学发现方面的潜力。通过提供这一全面概述，本综述旨在指导研究人员和临床医生了解病理图像分析领域的现状及其未来发展轨迹。
