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走向一个可解释的乳腺癌检测和诊断系统

Cristiana Moroz-Dubenco¹, Adél Bajcsi¹, Anca Andreica¹

¹Babeş-Bolyai University, Mihail Kogălniceanu 1, Cluj-Napoca, 400084, Cluj, Romania.

Computers in biology and medicine

|December 17, 2024

概括

这项研究引入了一个可解释的计算机辅助系统,用于乳腺癌的检测和诊断. 该自动化系统实现了高准确度,精度和特异性,帮助放射科医生进行乳房图解读.

科学领域:

医学成像分析医学成像分析
医疗保健中的人工智能
在瘤学瘤学.

背景情况:

乳腺癌查通过乳房扫描至关重要,但解释可能很困难.
计算机辅助检测和诊断 (CAD) 系统提供了第二意见,但需要对临床信任进行解释.
了解CAD系统决策对于有效整合到临床工作流程至关重要.

研究的目的:

开发用于乳腺癌检测和诊断的自动化和可解释的系统.
为了提高CAD系统在乳房镜中的可靠性和可信度.
为使用人工智能进行乳腺癌查的临床医生提供透明的决策过程.

主要方法:

该系统涉及预处理,无监督的细分,以及对基于纹理和形状的特征的分析.
用特征选择技术和决策树算法来进行良性/恶性分类.
使用预修剪,后修剪和随机森林分类器来解决过度装配,以提高可解释性.

主要成果:

拟议的系统在迷你MIAS数据集上实现了最大95%的准确性.
该系统在乳腺癌检测方面表现出100%的精度和100%的特异性.
该系统允许用户分析检测和诊断过程中的每个步骤.

结论:

成功开发了一种用于乳腺癌检测和诊断的自动化和可解释的系统.
该系统提供高诊断性能,与专家口译相提并论.
可解释性功能促进了信任,并促进了人工智能在乳房镜检查中的采用.

关键词:

乳腺组织细分的细分数字乳房影像处理数字乳房影像处理.可以解释性解释性损伤的分类损伤的分类

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