  Automated region of interest detection in histopathological image analysis is
a challenging and important topic with tremendous potential impact on clinical
practice. The deep-learning methods used in computational pathology may help us
to reduce costs and increase the speed and accuracy of cancer diagnosis. We
started with the UNC Melanocytic Tumor Dataset cohort that contains 160
hematoxylin and eosin whole-slide images of primary melanomas (86) and nevi
(74). We randomly assigned 80% (134) as a training set and built an in-house
deep-learning method to allow for classification, at the slide level, of nevi
and melanomas. The proposed method performed well on the other 20% (26) test
dataset; the accuracy of the slide classification task was 92.3% and our model
also performed well in terms of predicting the region of interest annotated by
the pathologists, showing excellent performance of our model on melanocytic
skin tumors. Even though we tested the experiments on the skin tumor dataset,
our work could also be extended to other medical image detection problems to
benefit the clinical evaluation and diagnosis of different tumors.
