由于人口老龄化和医疗保健计划的扩大，诊断组织病理学面临着日益增长的需求。采用深度学习方法的半自动诊断系统是缓解这种压力的一种方法。从用户的角度来看，组织病理学的学习模型本质上是复杂且不透明的。因此，人们开发了不同的方法来解释他们的行为。然而，对解释方法与经验丰富的病理学家知识之间的联系的关注相对有限。本文的主要贡献是一种方法，用于将专家病理学家用于检测癌症的形态模式与被认为对学习模型推理重要的模式进行比较。考虑到处理大规模组织病理学成像的基于斑块的性质，我们已经能够通过统计数据表明，在给定斑块大小和扫描分辨率的情况下，VGG16 模型可以利用病理学家可观察到的所有结构。结果表明，神经网络识别前列腺癌的方法与病理学家在中等光学分辨率下的方法相似。显着性图确定了表征癌症的几个主要组织形态学特征，例如单层上皮、小管腔和带有晕圈的深染核。一个令人信服的发现是在非肿瘤组织中识别出它们的模仿者。该方法还可以识别差异，即学习模型未使用的标准模式和病理学家尚未使用的新模式。显着图为自动化数字病理学提供附加值，以分析和微调深度学习系统并提高对基于计算机的决策的信任。版权所有 © 2023。由 Elsevier B.V. 出版。

Diagnostic histopathology faces increasing demands due to aging populations and expanding healthcare programs. Semi-automated diagnostic systems employing deep learning methods are one approach to alleviate this pressure. The learning models for histopathology are inherently complex and opaque from the user's perspective. Hence different methods have been developed to interpret their behavior. However, relatively limited attention has been devoted to the connection between interpretation methods and the knowledge of experienced pathologists. The main contribution of this paper is a method for comparing morphological patterns used by expert pathologists to detect cancer with the patterns identified as important for inference of learning models. Given the patch-based nature of processing large-scale histopathological imaging, we have been able to show statistically that the VGG16 model could utilize all the structures that are observable by the pathologist, given the patch size and scan resolution. The results show that the neural network approach to recognizing prostatic cancer is similar to that of a pathologist at medium optical resolution. The saliency maps identified several prevailing histomorphological features characterizing carcinoma, e.g., single-layered epithelium, small lumina, and hyperchromatic nuclei with halo. A convincing finding was the recognition of their mimickers in non-neoplastic tissue. The method can also identify differences, i.e., standard patterns not used by the learning models and new patterns not yet used by pathologists. Saliency maps provide added value for automated digital pathology to analyze and fine-tune deep learning systems and improve trust in computer-based decisions.Copyright © 2023. Published by Elsevier B.V.