Assisting Barrett's esophagus identification using endoscopic data augmentation based on Generative Adversarial Networks

Author(s)

de Souza Jr, Luis A. ; Passos, Leandro A. ^[1] ; Mendel, Robert ^{[2, 3]} ; Ebigbo, Alanna ^[4] ; Probst, Andreas ^[4] ; Messmann, Helmut ^[4] ; Palm, Christoph ^{[2, 3]} ; Papa, Joao P. ^[1]

Affiliation

^[1] Sao Paulo State Univ, Dept Comp, UNESP, Sao Paulo - Brazil

^[2] de Souza Jr, Jr., Luis A., Ostbayer TH Regensburg OTH Regensburg, Regensburg Med Image Comp ReMIC, Regensburg - Germany

^[3] Ostbayer TH Regensburg OTH Regensburg, Regensburg Ctr Hlth Sci & Technol RCHST, Regensburg - Germany

^[4] Univ Hosp Augsburg, Dept Gastroenterol, Augsburg - Germany

• Document type: Journal article
• Source: COMPUTERS IN BIOLOGY AND MEDICINE; v. 126, NOV 2020.
• Web of Science Citations: 1

Abstract

Barrett's esophagus figured a swift rise in the number of cases in the past years. Although traditional diagnosis methods offered a vital role in early-stage treatment, they are generally time- and resource-consuming. In this context, computer-aided approaches for automatic diagnosis emerged in the literature since early detection is intrinsically related to remission probabilities. However, they still suffer from drawbacks because of the lack of available data for machine learning purposes, thus implying reduced recognition rates. This work introduces Generative Adversarial Networks to generate high-quality endoscopic images, thereby identifying Barrett's esophagus and adenocarcinoma more precisely. Further, Convolution Neural Networks are used for feature extraction and classification purposes. The proposed approach is validated over two datasets of endoscopic images, with the experiments conducted over the full and patch-split images. The application of Deep Convolutional Generative Adversarial Networks for the data augmentation step and LeNet-5 and AlexNet for the classification step allowed us to validate the proposed methodology over an extensive set of datasets (based on original and augmented sets), reaching results of 90% of accuracy for the patch-based approach and 85% for the image-based approach. Both results are based on augmented datasets and are statistically different from the ones obtained in the original datasets of the same kind. Moreover, the impact of data augmentation was evaluated in the context of image description and classification, and the results obtained using synthetic images outperformed the ones over the original datasets, as well as other recent approaches from the literature. Such results suggest promising insights related to the importance of proper data for the accurate classification concerning computerassisted Barrett's esophagus and adenocarcinoma detection. (AU)

• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: José Alberto Cuminato
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 19/07665-4 - Center for Artificial Intelligence
• Grantee: Fabio Gagliardi Cozman
• Support Opportunities: Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program
• FAPESP's process: 19/08605-5 - Computer-assisted diagnosis of Barretts's esophagus using machine learning techniques
• Grantee: Luis Antonio de Souza Júnior
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• FAPESP's process: 17/04847-9 - Barrett's Esophagus Assisted Diagnosis Using Machine Learning
• Grantee: Luis Antonio de Souza Júnior
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants