Computer-assisted diagnosis of Barretts's esophagus using machine learning techniques

• Grant number: 19/08605-5
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: September 23, 2019
• End date: September 22, 2020
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: João Paulo Papa
• Grantee: Luis Antonio de Souza Júnior
• Supervisor: Christoph Palm
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
• Institution abroad: Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Germany
• Associated to the scholarship: 17/04847-9 - Barrett's Esophagus Assisted Diagnosis Using Machine Learning, BP.DR

Abstract

Esophageal adenocarcinoma is an illness that is usually hard to detect at the early stages in the presence of Barrett's esohagus. The development of automatic evaluation systems of such illness may be very useful, thus assisting the experts in the neoplastic region detection. With the strong growth of machine learning techniques aiming to improve the effectivess of medical diagnosis, the use of such approaches characterizes a strong scenario to be explored for the early diagnosis of esophageal adenocarcinoma. Barrett's esophagus as a predecessor of adenocarcinoma can be explained by some risk factors, such as obesity, smoking, and late medical diagnosis. This project proposes the development of new computer vision- and machine learning-driven techniques to assist the automatic diagnosis of the esophageal adenocarcinioma. This abroad proposal relates the time in which Professor Christoph Palm, who is the co-supervisor of such project being developed in Brazil, will help and supervise the project's development. Professor Palm provides a substancial and very important contribution to the proposed work, considering his strong and consistent research related to the computer-assisted evaluation of medical images. In addition, the abroad research group headed by Professor Palm (ReMIC) also presents relevant knowlegde related to the medical imaging processing area, and this can significantlly improve the Barrett's esophagus research results. It will be provided, during the abroad period, new endoscopic databases for the evaluation of the already studied handcrafted features. Also, the student will start the evaluation of adenocarcinoma and Barrett's esophagus differentiation using deep learning techniques, assisted by Professor Palm and the ReMIC team members. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DE SOUZA JR, LUIS A.; PASSOS, LEANDRO A.; MENDEL, ROBERT; EBIGBO, ALANNA; PROBST, ANDREAS; MESSMANN, HELMUT; PALM, CHRISTOPH; PAPA, JOAO P.. Assisting Barrett's esophagus identification using endoscopic data augmentation based on Generative Adversarial Networks. COMPUTERS IN BIOLOGY AND MEDICINE, v. 126, NOV 2020. (13/07375-0, 19/07665-4, 19/08605-5, 17/04847-9, 14/12236-1)

DE SOUZA JR, LUIS A.; MENDEL, ROBERT; STRASSER, SOPHIA; EBIGBO, ALANNA; PROBST, ANDREAS; MESSMANN, HELMUT; PAPA, JOAO P.; PALM, CHRISTOPH. Convolutional Neural Networks for the evaluation of cancer in Barrett's esophagus: Explainable AI to lighten up the black-box. COMPUTERS IN BIOLOGY AND MEDICINE, v. 135, AUG 2021. (17/04847-9, 16/19403-6, 14/12236-1, 13/07375-0, 19/08605-5)