Nature Inspired Computer Intelligence (NICI) Lab, Ontario Tech University, Oshawa, Ontario, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada.
Nature Inspired Computer Intelligence (NICI) Lab, Ontario Tech University, Oshawa, Ontario, Canada.
Am J Pathol. 2021 Dec;191(12):2172-2183. doi: 10.1016/j.ajpath.2021.08.013. Epub 2021 Sep 8.
Although deep learning networks applied to digital images have shown impressive results for many pathology-related tasks, their black-box approach and limitation in terms of interpretability are significant obstacles for their widespread clinical utility. This study investigates the visualization of deep features (DFs) to characterize two lung cancer subtypes, adenocarcinoma and squamous cell carcinoma. It demonstrates that a subset of DFs, called prominent DFs, can accurately distinguish these two cancer subtypes. Visualization of such individual DFs allows for a better understanding of histopathologic patterns at both the whole-slide and patch levels, and discrimination of these cancer types. These DFs were visualized at the whole slide image level through DF-specific heatmaps and at tissue patch level through the generation of activation maps. In addition, these prominent DFs can distinguish carcinomas of organs other than the lung. This framework may serve as a platform for evaluating the interpretability of any deep network for diagnostic decision making.
尽管应用于数字图像的深度学习网络在许多与病理学相关的任务中显示出了令人印象深刻的结果，但它们的黑盒方法和可解释性方面的限制是其广泛临床应用的重大障碍。本研究旨在通过可视化深度特征（DFs）来对两种肺癌亚型（腺癌和鳞状细胞癌）进行特征描述。研究表明，DFs 的一个子集，即突出 DFs，可以准确地区分这两种癌症亚型。对这些单个 DFs 的可视化可以更好地理解全切片和斑块水平的组织病理学模式，并对这些癌症类型进行区分。这些 DFs 通过特定于 DF 的热图在全幻灯片图像水平上进行可视化，并通过激活图的生成在组织斑块水平上进行可视化。此外，这些突出的 DFs 可以区分除肺以外的其他器官的癌症。该框架可作为评估任何用于诊断决策的深度学习网络的可解释性的平台。
