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深度学习在组织病理学中的应用:通往临床的道路。

Deep learning in histopathology: the path to the clinic.

机构信息

Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands.

Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.

出版信息

Nat Med. 2021 May;27(5):775-784. doi: 10.1038/s41591-021-01343-4. Epub 2021 May 14.

Abstract

Machine learning techniques have great potential to improve medical diagnostics, offering ways to improve accuracy, reproducibility and speed, and to ease workloads for clinicians. In the field of histopathology, deep learning algorithms have been developed that perform similarly to trained pathologists for tasks such as tumor detection and grading. However, despite these promising results, very few algorithms have reached clinical implementation, challenging the balance between hope and hype for these new techniques. This Review provides an overview of the current state of the field, as well as describing the challenges that still need to be addressed before artificial intelligence in histopathology can achieve clinical value.

摘要

机器学习技术在提高医学诊断方面具有巨大的潜力,为提高准确性、可重复性和速度提供了途径,并为临床医生减轻了工作负担。在组织病理学领域,已经开发出了深度学习算法,这些算法在肿瘤检测和分级等任务上的表现与经过训练的病理学家相当。然而,尽管取得了这些有希望的结果,但很少有算法真正应用于临床,这使得这些新技术的实际效果与人们的期望之间存在差距。本文综述了该领域的现状,并描述了在组织病理学中的人工智能达到临床价值之前仍需要解决的挑战。

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