HistoLens: a generalizable tool for increasing accessibility and interpretability of quantitative analyses in digital pathology

Samuel P. Border; Brandon Ginley; John E. Tomaszewski; Pinaki Sarder

doi:10.1117/12.2613503

4 April 2022 HistoLens: a generalizable tool for increasing accessibility and interpretability of quantitative analyses in digital pathology

Samuel P. Border, Brandon Ginley, John E. Tomaszewski, Pinaki Sarder

Author Affiliations +

Proceedings Volume 12039, Medical Imaging 2022: Digital and Computational Pathology; 120390S (2022) https://doi.org/10.1117/12.2613503
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

The incorporation of automated computational tools has a great amount of potential to positively influence the field of pathology. However, pathologists and regulatory agencies are reluctant to trust the output of complex models such as Convolutional Neural Networks (CNNs) due to their usual implementation as black-box tools. Increasing the interpretability of quantitative analyses is a critical line of research in order to increase the adoption of modern Machine Learning (ML) pipelines in clinical environments. Towards that goal, we present HistoLens, a Graphical User Interface (GUI) designed to facilitate quantitative assessments of datasets of annotated histological compartments. Additionally, we introduce the use of hand-engineered feature visualizations to highlight regions within each structure that contribute to particular feature values. These feature visualizations can then be paired with feature hierarchy determinations in order to view which regions within an image are significant to a particular sub-group within the dataset. As a use case, we analyzed a dataset of old and young mouse kidney sections with glomeruli annotated. We highlight some of the functional components within HistoLens that allow non-computational experts to efficiently navigate a new dataset as well as allowing for easier transition to downstream computational analyses.

Conference Presentation

Citation Download Citation

Samuel P. Border, Brandon Ginley, John E. Tomaszewski, and Pinaki Sarder "HistoLens: a generalizable tool for increasing accessibility and interpretability of quantitative analyses in digital pathology", Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 120390S (4 April 2022); https://doi.org/10.1117/12.2613503

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