Intrapapillary capillary loop classification in magnification endoscopy: open dataset and baseline methodology.

ABSTRACT:

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 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 labelled frames extracted from 114 patient videos whose imaged areas have been resected and correlated to histopathology. Our novel convolutional network 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% a

SUBMITTER: Garcia-Peraza-Herrera LC 

PROVIDER: S-EPMC7142046 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature