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

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.

Keywords: artificial intelligence; deep learning; neuropathology.

Figure 1

Overview of machine learning framework in digital pathology. (A) The commonly used method for gathering training data from H&E-stained histopathology images, where smaller patches are extracted from the gigapixel WSIs. (B) Examples of labels paired with histopathology image data, note the expertise required to generate this information. (C) Training frameworks used in machine learning that are frequently used in digital pathology contexts for a variety of different tasks.

Figure 2

Examples of methods for deep learning explainability in digital pathology. (A) Thumbnail of FFPE WSI from TCGA-LGG dataset (B) Thumbnail from (A) with overlaid attention mapping from an attention-based MIL model. (C) Patch extracted at 40× magnification from (A,D) Grad-CAM activation mapping.