Kavli Affiliate: Changhuei Yang
| First 5 Authors: Haowen Zhou, Steven, Lin, Mark Watson, Cory T. Bernadt
| Summary:
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.
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