Politehnica University of Timişoara, Timişoara, Romania.
Stud Health Technol Inform. 2024 Aug 22;316:1003-1007. doi: 10.3233/SHTI240579.
The digital pathology landscape is in continuous expansion. The digitalization of slides using WSIs (Whole Slide Images) fueled the capacity of automatic support for diagnostics. The paper presents an overview of the current state of the art methods used in histopathological practice for explaining CNN classification useful for histopathological experts. Following the study we observed that histopathological deep learning models are still underused and that the pathologists do not trust them. Also we need to point out that in order to get a sustainable use of deep learning we need to get the experts to trust the models. In order to do that, they need to understand how the results are generated and how this information correlates with their prior knowledge and for obtaining this they can use the methods highlighted in this study.
数字病理学领域正在不断扩展。使用 WSI（全玻片图像）对幻灯片进行数字化，为自动诊断支持提供了动力。本文概述了当前在组织病理学实践中用于解释 CNN 分类的最新方法，这些方法对组织病理学专家很有用。通过研究我们观察到，组织病理学深度学习模型仍未得到充分利用，病理学家也不信任它们。此外，我们还需要指出，为了实现深度学习的可持续使用，我们需要让专家信任这些模型。为了做到这一点，他们需要了解结果是如何生成的，以及这些信息如何与他们的先验知识相关，为了获得这些信息，他们可以使用本研究中强调的方法。
