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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Barrett's esophagus analysis using infinity Restricted Boltzmann Machines

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Passos, Leandro A. [1] ; de Souza, Jr., Luis A. [1] ; Mendel, Robert [2, 3] ; Ebigbo, Alanna [4] ; Probst, Andreas [4] ; Messmann, Helmut [4] ; Palm, Christoph [2, 3] ; Papa, Joao Paulo [5]
Total Authors: 8
[1] Univ Fed Sao Carlos, UFSCAR, Dept Comp, BR-13565905 Sao Carlos, SP - Brazil
[2] OTH Regensburg, Ostbayer Tech Hsch Regensburg, Regensburg Med Image Comp ReMIC, D-93053 Regensburg - Germany
[3] OTH Regensburg, Regensburg Ctr Hlth Sci & Technol, D-93053 Regensburg - Germany
[4] Klinikum Augsburg III, Med Klin, D-86156 Augsburg - Germany
[5] Sao Paulo State Univ, UNESP, Dept Comp, BR-17033360 Bauru - Brazil
Total Affiliations: 5
Document type: Journal article
Web of Science Citations: 0

The number of patients with Barret's esophagus (BE) has increased in the last decades. Considering the dangerousness of the disease and its evolution to adenocarcinoma, an early diagnosis of BE may provide a high probability of cancer remission. However, limitations regarding traditional methods of detection and management of BE demand alternative solutions. As such, computer-aided tools have been recently used to assist in this problem, but the challenge still persists. To manage the problem, we introduce the infinity Restricted Boltzmann Machines (iRBMs) to the task of automatic identification of Barrett's esophagus from endoscopic images of the lower esophagus. Moreover, since iRBM requires a proper selection of its meta-parameters, we also present a discriminative iRBM fine-tuning using six meta-heuristic optimization techniques. We showed that iRBMs are suitable for the context since it provides competitive results, as well as the meta-heuristic techniques showed to be appropriate for such task. (C) 2019 Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms
Grantee:João Paulo Papa
Support type: Regular Research Grants
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 type: Research Projects - Thematic Grants
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:José Alberto Cuminato
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 15/25739-4 - Ón “The study of semantics ín deep learning models
Grantee:Gustavo Henrique de Rosa
Support type: Scholarships in Brazil - Master
FAPESP's process: 16/21243-7 - Learning Dropout Parameters for Convolutional Neural Networks
Grantee:Gustavo Henrique de Rosa
Support type: Scholarships abroad - Research Internship - Master's degree