  Background: Margin assessment of basal cell carcinoma using the frozen
section is a common task of pathology intraoperative consultation. Although
frequently straight-forward, the determination of the presence or absence of
basal cell carcinoma on the tissue sections can sometimes be challenging. We
explore if a deep learning model trained on mobile phone-acquired frozen
section images can have adequate performance for future deployment. Materials
and Methods: One thousand two hundred and forty-one (1241) images of frozen
sections performed for basal cell carcinoma margin status were acquired using
mobile phones. The photos were taken at 100x magnification (10x objective). The
images were downscaled from a 4032 x 3024 pixel resolution to 576 x 432 pixel
resolution. Semantic segmentation algorithm Deeplab V3 with Xception backbone
was used for model training. Results: The model uses an image as input and
produces a 2-dimensional black and white output of prediction of the same
dimension; the areas determined to be basal cell carcinoma were displayed with
white color, in a black background. Any output with the number of white pixels
exceeding 0.5% of the total number of pixels is deemed positive for basal cell
carcinoma. On the test set, the model achieves area under curve of 0.99 for
receiver operator curve and 0.97 for precision-recall curve at the pixel level.
The accuracy of classification at the slide level is 96%. Conclusions: The deep
learning model trained with mobile phone images shows satisfactory performance
characteristics, and thus demonstrates the potential for deploying as a mobile
phone app to assist in frozen section interpretation in real time.
