文章：

深度学习在诊断神经病理学中的实际应用——精准医疗时代下组织学资产的再构想

Practical Application of Deep Learning in Diagnostic Neuropathology—Reimagining a Histological Asset in the Era of Precision Medicine

原文发布日期：23 May 2024

DOI: 10.3390/cancers16111976

类型: Article

开放获取: 是

英文摘要：

In the past few decades, neuropathology has experienced several paradigm shifts with the introduction of new technologies. Deep learning, a rapidly progressing subfield of machine learning, seems to be the next innovation to alter the diagnostic workflow. In this review, we will explore the recent changes in the field of neuropathology and how this has led to an increased focus on molecular features in diagnosis and prognosis. Then, we will examine the work carried out to train deep learning models for various diagnostic tasks in neuropathology, as well as the machine learning frameworks they used. Focus will be given to both the challenges and successes highlighted therein, as well as what these trends may tell us about future roadblocks in the widespread adoption of this new technology. Finally, we will touch on recent trends in deep learning, as applied to digital pathology more generally, and what this may tell us about the future of deep learning applications in neuropathology.

摘要翻译： 

在过去的几十年里，随着新技术的引入，神经病理学领域经历了数次范式转变。深度学习作为机器学习中快速发展的子领域，似乎将成为改变诊断流程的下一个创新点。本文综述将探讨神经病理学领域的最新变革，以及这些变革如何促使诊断与预后评估更加关注分子特征。随后，我们将系统梳理针对神经病理学各类诊断任务所开展的深度学习模型训练工作，并分析其采用的机器学习框架。重点将聚焦于研究中凸显的挑战与成功案例，同时探讨这些趋势对新技术广泛普及过程中可能遇到的未来障碍的启示。最后，我们将概述深度学习在更广义数字病理学中的最新发展趋势，并据此展望深度学习在神经病理学领域的应用前景。

原文链接：

Practical Application of Deep Learning in Diagnostic Neuropathology—Reimagining a Histological Asset in the Era of Precision Medicine