  Purpose. Early squamous cell neoplasia (ESCN) in the oesophagus is a highly
treatable condition. Lesions confined to the mucosal layer can be curatively
treated endoscopically. We build a computer-assisted detection (CADe) system
that can classify still images or video frames as normal or abnormal with high
diagnostic accuracy. Methods. We present a new benchmark dataset containing 68K
binary labeled frames extracted from 114 patient videos whose imaged areas have
been resected and correlated to histopathology. Our novel convolutional network
(CNN) architecture solves the binary classification task and explains what
features of the input domain drive the decision-making process of the network.
Results. The proposed method achieved an average accuracy of 91.7 % compared to
the 94.7 % achieved by a group of 12 senior clinicians. Our novel network
architecture produces deeply supervised activation heatmaps that suggest the
network is looking at intrapapillary capillary loop (IPCL) patterns when
predicting abnormality. Conclusion. We believe that this dataset and baseline
method may serve as a reference for future benchmarks on both video frame
classification and explainability in the context of ESCN detection. A future
work path of high clinical relevance is the extension of the classification to
ESCN types.
