  Standard of care diagnostic procedure for suspected skin cancer is
microscopic examination of hematoxylin \& eosin stained tissue by a
pathologist. Areas of high inter-pathologist discordance and rising biopsy
rates necessitate higher efficiency and diagnostic reproducibility. We present
and validate a deep learning system which classifies digitized dermatopathology
slides into 4 categories. The system is developed using 5,070 images from a
single lab, and tested on an uncurated set of 13,537 images from 3 test labs,
using whole slide scanners manufactured by 3 different vendors. The system's
use of deep-learning-based confidence scoring as a criterion to consider the
result as accurate yields an accuracy of up to 98\%, and makes it adoptable in
a real-world setting. Without confidence scoring, the system achieved an
accuracy of 78\%. We anticipate that our deep learning system will serve as a
foundation enabling faster diagnosis of skin cancer, identification of cases
for specialist review, and targeted diagnostic classifications.
