  Pathology slides of lung malignancies are classified using resource-frugal
convolution neural networks (CNNs) that may be deployed on mobile devices. In
particular, the challenging task of distinguishing adenocarcinoma (LUAD) and
squamous-cell carcinoma (LUSC) lung cancer subtypes is approached in two
stages. First, whole-slide histopathology images are downsampled to a size too
large for CNN analysis but large enough to retain key anatomic detail. The
downsampled images are decomposed into smaller square tiles, which are sifted
based on their image entropies. A lightweight CNN produces tile-level
classifications that are aggregated to classify the slide. The resulting
accuracies are comparable to those obtained with much more complex CNNs and
larger training sets. To allow clinicians to visually assess the basis for the
classification -- that is, to see the image regions that underlie it --
color-coded probability maps are created by overlapping tiles and averaging the
tile-level probabilities at a pixel level.
