Intrapapillary Capillary Loop Classification in Magnification Endoscopy: Open Dataset and Baseline Methodology

• URL: http://arxiv.org/abs/2102.09963v1
• Date: Fri, 19 Feb 2021 14:55:21 GMT
• Title: Intrapapillary Capillary Loop Classification in Magnification Endoscopy: Open Dataset and Baseline Methodology
• Authors: Luis C. Garcia-Peraza-Herrera, Martin Everson, Laurence Lovat, Hsiu-Po Wang, Wen Lun Wang, Rehan Haidry, Danail Stoyanov, Sebastien Ourselin, Tom Vercauteren
• Abstract summary: We build a computer-assisted detection system that can classify still images or video frames as normal or abnormal. We present a new benchmark dataset containing 68K binary labeled frames extracted from 114 patient videos. The proposed method achieved an average accuracy of 91.7 % compared to the 94.7 % achieved by a group of 12 senior clinicians.
• Score: 8.334256673330879
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 (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.

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