Explainable AI for computational pathology identifies model limitations and tissue biomarkers.

ArXiv Pub Date : 2024-09-04
Jakub R Kaczmarzyk, Joel H Saltz, Peter K Koo
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Abstract

Deep learning models have shown promise in histopathology image analysis, but their opaque decision-making process poses challenges in high-risk medical scenarios. Here we introduce HIPPO, an explainable AI method that interrogates attention-based multiple instance learning (ABMIL) models in computational pathology by generating counterfactual examples through tissue patch modifications in whole slide images. Applying HIPPO to ABMIL models trained to detect breast cancer metastasis reveals that they may overlook small tumors and can be misled by non-tumor tissue, while attention maps-widely used for interpretation-often highlight regions that do not directly influence predictions. By interpreting ABMIL models trained on a prognostic prediction task, HIPPO identified tissue areas with stronger prognostic effects than high-attention regions, which sometimes showed counterintuitive influences on risk scores. These findings demonstrate HIPPO's capacity for comprehensive model evaluation, bias detection, and quantitative hypothesis testing. HIPPO greatly expands the capabilities of explainable AI tools to assess the trustworthy and reliable development, deployment, and regulation of weakly-supervised models in computational pathology.

用于计算病理学的可解释人工智能可识别模型限制和组织生物标记。
深度学习模型在组织病理学图像分析中大有可为,但其不透明的决策过程给高风险医疗场景带来了挑战。在此,我们介绍一种可解释的人工智能方法--HIPPO,该方法通过整张切片图像中的组织斑块修改生成反事实示例,在计算病理学中对基于注意力的多实例学习(ABMIL)模型进行检验。将 HIPPO 应用于为检测乳腺癌转移而训练的 ABMIL 模型时发现,这些模型可能会忽略小肿瘤,并可能被非肿瘤组织误导,而广泛用于解释的注意力图谱往往会突出显示不直接影响预测的区域。通过解释在预后预测任务中训练的 ABMIL 模型,HIPPO 发现了比高注意力区域具有更强预后效应的组织区域,而高注意力区域有时会对风险评分产生反直觉的影响。这些发现证明了 HIPPO 在综合模型评估、偏差检测和定量假设检验方面的能力。HIPPO 极大地扩展了可解释人工智能工具的能力,以评估计算病理学中弱监督模型的开发、部署和监管是否值得信赖和可靠。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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