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在基因病理学中的欺骗性学习

Sahar Shahamatdar^1,2, Daryoush Saeed-Vafa³, Drew Linsley^4,5

¹Center for Computational Molecular Biology, Brown University, Providence, RI, USA.

|April 1, 2024

概括

深度神经网络 (DNN) 在组织病理学方面表现有前途,但可以学习欺骗性策略. 虽然DNN对于瘤检测是值得信赖的,但由于虚假的相关性,DNN无法对分子分析进行概括.

科学领域:

计算病理学计算病理学
医学中的人工智能
组织病理学图像分析分析.

背景情况:

深度学习 (DL) 提供了自动化组织病理学任务和发现新生物学见解的潜力.
缺乏对DL模型在组织病理学中学习的视觉策略可靠性的系统评估.

研究的目的:

系统地评估深度神经网络 (DNN),训练有素的组织病理学分析.
为了确定DNN的学习策略是否值得信赖或欺骗.

主要方法:

在221张肺腺癌全幻灯片图像 (WSI) 上训练了各种DNN.
在分子分析 (KRAS与EGFR突变),初级组织确定和瘤检测方面评估了DNA.

主要成果:

通过利用组织学亚型和突变之间的相关性,DNN在分子分析中取得了超越机会的表现,但未能将其泛化为激光捕捉微解剖 (LCM) 试验集.
DNNs学习了用于初级组织确定和瘤检测/定位的强大和可靠的策略.

结论:

DNN显示出帮助病理学家的希望,但可以学习使用虚假关联的欺骗性策略,使它们不适合研究或临床使用.
提出的模型评估和解释框架对于开发可靠的自动化组织病理学分析系统至关重要.

关键词:

克拉斯 (Kras) 是一个国家.计算病理学计算病理学深度学习是一种深度学习.可解释的人工智能分子造型分析 (MOP) 是一种分子造型分析.

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Fixation and Sectioning

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