Novel Deep Learning Methodology for Automated Classification of Adamantinomatous Craniopharyngioma Using a Small Radiographic Dataset

Modern Deep Learning (DL) networks routinely achieve classification accuracy superior to human experts, leveraging scenarios with vast amounts of training data. Community focus has now seen a shift towards the design of accurate classifiers for scenarios with limited training data. Such an example is the uncommon pediatric brain tumor, Adamantinomatous Craniopharyngioma (ACP). Recent work has demonstrated the efficacy of Transfer Learning (TL) and novel loss functions for the training of DL networks in limited data scenarios. This work describes a DL approach utilizing TL and a state-of-the-art custom loss function for predicting ACP diagnosis from radiographic data, achieving performance (CT AUPR=0.99+/-0.01, MRI AUPR=0.99+/-0.02) superior to reported human performance (0.87).