Practical Application of Deep Learning in Diagnostic Neuropathology—Reimagining a Histological Asset in the Era of Precision Medicine

Author:

Rich Katherine1ORCID,Tosefsky Kira2ORCID,Martin Karina C.23ORCID,Bashashati Ali34,Yip Stephen23ORCID

Affiliation:

1. Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC V6T 1Z4, Canada

2. Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada

3. Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada

4. School of Biomedical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada

Abstract

In the past few decades, neuropathology has experienced several paradigm shifts with the introduction of new technologies. Deep learning, a rapidly progressing subfield of machine learning, seems to be the next innovation to alter the diagnostic workflow. In this review, we will explore the recent changes in the field of neuropathology and how this has led to an increased focus on molecular features in diagnosis and prognosis. Then, we will examine the work carried out to train deep learning models for various diagnostic tasks in neuropathology, as well as the machine learning frameworks they used. Focus will be given to both the challenges and successes highlighted therein, as well as what these trends may tell us about future roadblocks in the widespread adoption of this new technology. Finally, we will touch on recent trends in deep learning, as applied to digital pathology more generally, and what this may tell us about the future of deep learning applications in neuropathology.

Funder

NSERC Discovery Grant

Michael Smith Health Research BC

Publisher

MDPI AG

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