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用于异常检测的学习图像表示:用于发现药物开发中的组织学变化的应用

Igor Zingman¹, Birgit Stierstorfer², Charlotte Lempp¹

¹Drug Discovery Sciences, Boehringer Ingelheim Pharma GmbH and Co., Biberach an der Riß, Germany.

Medical image analysis

|December 23, 2023

概括

这项研究引入了一种新的系统,用于检测异常在他的病理图像. 该方法增强了卷积神经网络 (CNN) 表示,以改善罕见病理病例的检测,帮助早期评估药物毒性.

科学领域:

数字病理学数字病理学
计算生物学计算生物学
医学成像分析医学成像分析

背景情况:

由于病理样本的稀缺性,在组织病理学中检测异常具有挑战性.
使用预训练的卷积神经网络 (CNN) 表示的现有方法可能缺乏对微妙组织异常的敏感性.
健康组织中的自然变异可能导致不准确的表示.

研究的目的:

开发一种改进的系统,用于检测异常在他的病理图像.
调整CNN表示,以更好地捕捉健康组织中的相关细节,以检测异常.
为了使候选药物的早期毒性评估.

主要方法:

在辅助任务中训练CNN,在不同物种,器官和染色试剂中区分健康组织.
在训练期间使用中心损失术语来强制执行紧的图像表示.
利用一类分类器与适应的CNN表示来检测异常.

主要成果:

拟议的系统在肝脏异常数据集上优于已建立的异常检测 (AD) 方法.
取得了与肝脏异常量化的专业方法相似的结果.
在候选药物的早期毒性评估中证明有用.

结论:

开发的系统有效地解决了在组织病理学中有限的异常数据的挑战.
这种方法提高了CNN表示用于异常检测的灵敏度.
这种方法有可能通过早期毒性查来减少晚期药物消耗.

关键词:

卷积神经网络是一种卷积神经网络.深度功能功能功能功能深度功能毒性药物毒性毒性药物毒性一个类别的分类分类.异常值检测异常值的检测

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