人工智能在病理诊断领域的进展

余净纯; 郭明星; 韩靖; 张小鹰; 陈汉威; 王浩

doi:10.12122/j.issn.1674-4500.2022.05.29

人工智能在病理诊断领域的进展

doi: 10.12122/j.issn.1674-4500.2022.05.29

余净纯^{1, 2,},
郭明星³,
韩靖³,
张小鹰³,
陈汉威³,
王浩^3, ,

1.
华南师范大学-番禺区中心医院基础与转化医学联合实验室，广东广州 511400
2.
华南师范大学生命科学学院，广东广州 510630
3.
番禺区中心医院病理科，广东广州 511400

基金项目:

国家自然科学基金海外及港澳学者延续重点资助项目 81729003

详细信息

作者简介:
余净纯，在读硕士研究生，E-mail: jingchun5850@163.com

通讯作者:
王浩，硕士，副主任医师，E-mail: pyblk@hotmail.com

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- 文章访问数: 711
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- 被引次数: 0
出版历程
- 收稿日期: 2022-06-22
- 刊出日期: 2022-09-20

Progress of artificial intelligence in the field of pathological diagnosis

1.
South China Normal University-Panyu Central Hospital Joint Laboratory of Basis and Translational Medical Research, Guangzhou 511400, China
2.
College of Life Sciences, South China Normal University, Guangzhou 510630, China
3.
Department of Pathology, Guangzhou Panyu District Central Hospital, Guangzhou 511400, China

摘要

摘要: 基于近几年机器视觉的发展，深度学习的人工智能方法应用于组织病理极大程度上促进了病理学家解决临床上的诊断问题，用该种方法解决病理学问题可被称为计算机病理学。人工智能可以做到帮助病理学家初筛大部分良性数据、辅助诊断、疗效预测、识别生物标志物等，甚至可以做到对药效治疗监测以及识别药物发现未知的信号。基于深度学习在病理领域的深入研究，让计算机自动处理病理数据成为可能。人工智能诊断决策建立在大数据之上，很多有可能做到对每个病人的个性化管理，对于大多普遍性的疾病诊断有着更加快速准确的优势。但数字病理学的发展仍受到一些问题的限制，以至于现阶段没有广泛应用于数字病理诊断平台。本文总结了近几年人工智能在病理诊断领域的最新进展，并讨论这种技术的可行性，补充说明在数字病理学中遇到的困难和挑战，并提出在该领域实用性上的展望。
- 数字病理学 /
- 计算机辅助诊断 /
- 深度学习 /
- 全切片图像
Abstract: Based on the development of machine vision in recent years, the application of deep learning artificial intelligence methods in histopathology has greatly promoted pathologists to solve clinical diagnostic problems. This method can be called computer pathology. Artificial intelligence can help pathologists sift through most benign data, aid in diagnosis, predict efficacy, identify biomarkers, and even monitor therapeutic efficacy and identify unknown signals for drugs. Based on the in-depth study of deep learning in the field of pathology, it is possible for the computer to process pathological data automatically. Artificial intelligence diagnostic decisions are based on big data, and it is possible to personalize the management of each patient. It has the advantage of more rapid and accurate diagnosis for most common diseases. However, it is worth considering that the development of digital pathology is still limited by some problems, so that it is not widely used in digital pathology diagnostic platform at the present stage. We summarized the recent progress of artificial intelligence in the field of pathological diagnosis in this paper. We discussed the feasibility of this technology, added the difficulties and challenges encountered in digital pathology, and put forward the prospect of its practicality in this field.
- digital pathological /
- computer-aided diagnosis /
- deep learning /
- whole slide image

HTML全文

图 1 同一淋巴结病理切片

A: H & E; B: 免疫组化CD3; C: 免疫组化CD5; D: 免疫组化CD20; E: 免疫组化CD21; F: 免疫组化CD23.

Figure 1. Pathological sections of the same lymph node.

下载: 全尺寸图片幻灯片

图 2 用于检测图像中视觉类别的卷积神经网络的处理流程

Figure 2. Processing pipeline of convolution alneural network for the detection of visualcategoriesin images.

下载: 全尺寸图片幻灯片

表 1 公开的病理数据库

Table 1. Public pathology data sets

数据集	时间	类型	任务	图片数量
IICBY-2008	2008年	H & E	分类	274
Camelyon16	2016年	H & E	检测	训练270，测试130
Camelyon17	2017年	H & E	检测和分类	训练500，测试500
GTEx	2017年	H & E	检测和分类	25 440
TMAD^[60]	2007年	H & E/IHC	分类	31 306
TUPAC16^[47]	2017年	H & E	预测	821

下载: 导出CSV

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