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Barrett's Esophagus Assisted Diagnosis Using Machine Learning

Grant number: 17/04847-9
Support Opportunities:Scholarships in Brazil - Doctorate
Effective date (Start): February 01, 2019
Effective date (End): March 31, 2022
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:João Paulo Papa
Grantee:Luis Antonio de Souza Júnior
Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
Associated research grant:14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?, AP.TEM
Associated scholarship(s):19/08605-5 - Computer-assisted diagnosis of Barretts's esophagus using machine learning techniques, BE.EP.DR


The Barrett's Esophagus is usually hard to detect at the early stages of the esophageal lesion, being not correctly observed in the cancerous tissue evaluation so often. 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 the machine learning techniques aiming to improve the medical diagnosis efficiency, the use of such techniques characterize a strong scenario to be explored for the early diagnosis of Barrett's esophagus. Notice this disease is responsible for the growth of esophageal adenocarcinoma worldwide, which can explained by some risk factors, such as obesity, smoking and late medical diagnosis. Given that a number of works have used artificial intelligence tools to assist the automatic medical diagnosis, this project proposes the development of new computer vision- and machine learning-driven techniques to assist the automatic diagnosis of the Barrett's esophagus.

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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, . (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, . (17/04847-9, 16/19403-6, 14/12236-1, 13/07375-0, 19/08605-5)

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