  In recent years, deep learning has successfully been applied to automate a
wide variety of tasks in diagnostic histopathology. However, fast and reliable
localization of small-scale regions-of-interest (ROI) has remained a key
challenge, as discriminative morphologic features often occupy only a small
fraction of a gigapixel-scale whole-slide image (WSI). In this paper, we
propose a sparse WSI analysis method for the rapid identification of high-power
ROI for WSI-level classification. We develop an evaluation framework inspired
by the early classification literature, in order to quantify the tradeoff
between diagnostic performance and inference time for sparse analytic
approaches. We test our method on a common but time-consuming task in pathology
- that of diagnosing gastric intestinal metaplasia (GIM) on hematoxylin and
eosin (H&E)-stained slides from endoscopic biopsy specimens. GIM is a
well-known precursor lesion along the pathway to development of gastric cancer.
We performed a thorough evaluation of the performance and inference time of our
approach on a test set of GIM-positive and GIM-negative WSI, finding that our
method successfully detects GIM in all positive WSI, with a WSI-level
classification area under the receiver operating characteristic curve (AUC) of
0.98 and an average precision (AP) of 0.95. Furthermore, we show that our
method can attain these metrics in under one minute on a standard CPU. Our
results are applicable toward the goal of developing neural networks that can
easily be deployed in clinical settings to support pathologists in quickly
localizing and diagnosing small-scale morphologic features in WSI.
