Deep learning assisted digital pathology has the potential to impact clinical
practice in significant ways. In recent studies, deep neural network (DNN)
enabled analysis outperforms human pathologists. Increasing sizes and
complexity of the DNN architecture generally improves performance at the cost
of DNN's explainability. For pathology, this lack of DNN explainability is
particularly problematic as it hinders the broader clinical interpretation of
the pathology features that may provide physiological disease insights. To
better assess the features that DNN uses in developing predictive algorithms to
interpret digital microscopic images, we sought to understand the role of
resolution and tissue scale and here describe a novel method for studying the
predictive feature length-scale that underpins a DNN's predictive power. We
applied the method to study a DNN's predictive capability in the case example
of brain metastasis prediction from early-stage non-small-cell lung cancer
biopsy slides. The study highlights the DNN attention in the brain metastasis
prediction targeting both cellular scale (resolution) and tissue scale features
on H&E-stained histological whole slide images. At the cellular scale, we see
that DNN's predictive power is progressively increased at higher resolution
(i.e., lower resolvable feature length) and is largely lost when the resolvable
feature length is longer than 5 microns. In addition, DNN uses more macro-scale
features (maximal feature length) associated with tissue
organization/architecture and is optimized when assessing visual fields larger
than 41 microns. This study for the first time demonstrates the length-scale
requirements necessary for optimal DNN learning on digital whole slide images.

利用深度学习辅助数字病理学有潜力在临床实践中产生重大影响，但深度神经网络 (DNN) 的解释性不足成为制约其临床解释性的主要问题。研究旨在通过一种新方法来研究 DNN 的预测特征长度尺度，从而更好地评估 DNN 在解释数字显微镜图像中的特征使用。研究使用脑转移为例，突出了 DNN 在 H&E 染色组织切片图像上预测脑转移的注意力定位层次，有助于了解 DNN 的预测能力和其最佳学习要求。