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In recent years, deep learning has successfully been applied to automate awide variety of tasks in diagnostic histopathology. However, fast and reliablelocalization of small-scale regions-of-interest (ROI) has remained a keychallenge, as discriminative morphologic features often occupy only a smallfraction of a gigapixel-scale whole-slide image (WSI). In this paper, wepropose a sparse WSI analysis method for the rapid identification of high-powerROI for WSI-level classification. We develop an evaluation framework inspiredby the early classification literature, in order to quantify the tradeoffbetween diagnostic performance and inference time for sparse analyticapproaches. We test our method on a common but time-consuming task in pathology- that of diagnosing gastric intestinal metaplasia (GIM) on hematoxylin andeosin (H&E)-stained slides from endoscopic biopsy specimens. GIM is awell-known precursor lesion along the pathway to development of gastric cancer.We performed a thorough evaluation of the performance and inference time of ourapproach on a test set of GIM-positive and GIM-negative WSI, finding that ourmethod successfully detects GIM in all positive WSI, with a WSI-levelclassification area under the receiver operating characteristic curve (AUC) of0.98 and an average precision (AP) of 0.95. Furthermore, we show that ourmethod can attain these metrics in under one minute on a standard CPU. Ourresults are applicable toward the goal of developing neural networks that caneasily be deployed in clinical settings to support pathologists in quicklylocalizing and diagnosing small-scale morphologic features in WSI.

Deep Learning-Based Sparse Whole-Slide Image Analysis for the Diagnosis of Gastric Intestinal Metaplasia