Department of Medicine, Section of Hematology/Oncology, University of Chicago Medical Center, 5841 South Maryland Avenue, MC 2115, Chicago, IL 60637, USA; The University of Chicago Medicine & Biological Sciences, 5841 South Maryland Avenue, Chicago, IL, USA.
Department of Medicine, Section of Hematology/Oncology, University of Chicago Medical Center, 5841 South Maryland Avenue, MC 2115, Chicago, IL 60637, USA; University of Chicago Comprehensive Cancer Center, Chicago, IL, USA; The University of Chicago Medicine & Biological Sciences, 5841 South Maryland Avenue, Chicago, IL, USA.
Surg Pathol Clin. 2023 Mar;16(1):167-176. doi: 10.1016/j.path.2022.09.014. Epub 2022 Dec 12.
Machine learning methods have been growing in prominence across all areas of medicine. In pathology, recent advances in deep learning (DL) have enabled computational analysis of histological samples, aiding in diagnosis and characterization in multiple disease areas. In cancer, and particularly endocrine cancer, DL approaches have been shown to be useful in tasks ranging from tumor grading to gene expression prediction. This review summarizes the current state of DL research in endocrine cancer histopathology with an emphasis on experimental design, significant findings, and key limitations.
机器学习方法在医学的各个领域的重要性日益凸显。在病理学领域，深度学习（DL）的最新进展使得对组织学样本的计算分析成为可能，有助于在多个疾病领域进行诊断和特征描述。在癌症领域，特别是内分泌癌，DL 方法已被证明在从肿瘤分级到基因表达预测等任务中具有一定的作用。本文综述了内分泌癌组织病理学中深度学习研究的现状，重点介绍了实验设计、重要发现和关键限制。
