Learning visual representations with optimum-path forest and its applications to Barrett’s esophagus and adenocarcinoma diagnosis

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dc.contributor.authorde Souza, Luis A.
dc.contributor.authorAfonso, Luis C. S.
dc.contributor.authorEbigbo, Alanna
dc.contributor.authorProbst, Andreas
dc.contributor.authorMessmann, Helmut
dc.contributor.authorMendel, Robert
dc.contributor.authorHook, Christian
dc.contributor.authorPalm, Christoph
dc.contributor.authorPapa, João P. [UNESP]
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.contributor.institutionKlinikum Augsburg
dc.contributor.institutionOstbayerische Technische Hochschule Regensburg - OTH Regensburg
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2019-10-06T16:59:47Z
dc.date.available2019-10-06T16:59:47Z
dc.date.issued2019-01-01
dc.description.abstractConsidering the increase in the number of the Barrett’s esophagus (BE) in the last decade, and its expected continuous increase, methods that can provide an early diagnosis of dysplasia in BE-diagnosed patients may provide a high probability of cancer remission. The limitations related to traditional methods of BE detection and management encourage the creation of computer-aided tools to assist in this problem. In this work, we introduce the unsupervised Optimum-Path Forest (OPF) classifier for learning visual dictionaries in the context of Barrett’s esophagus (BE) and automatic adenocarcinoma diagnosis. The proposed approach was validated in two datasets (MICCAI 2015 and Augsburg) using three different feature extractors (SIFT, SURF, and the not yet applied to the BE context A-KAZE), as well as five supervised classifiers, including two variants of the OPF, Support Vector Machines with Radial Basis Function and Linear kernels, and a Bayesian classifier. Concerning MICCAI 2015 dataset, the best results were obtained using unsupervised OPF for dictionary generation using supervised OPF for classification purposes and using SURF feature extractor with accuracy nearly to 78 % for distinguishing BE patients from adenocarcinoma ones. Regarding the Augsburg dataset, the most accurate results were also obtained using both OPF classifiers but with A-KAZE as the feature extractor with accuracy close to 73 %. The combination of feature extraction and bag-of-visual-words techniques showed results that outperformed others obtained recently in the literature, as well as we highlight new advances in the related research area. Reinforcing the significance of this work, to the best of our knowledge, this is the first one that aimed at addressing computer-aided BE identification using bag-of-visual-words and OPF classifiers, being the application of unsupervised technique in the BE feature calculation the major contribution of this work. It is also proposed a new BE and adenocarcinoma description using the A-KAZE features, not yet applied in the literature.en
dc.description.affiliationDepartment of Computing Federal University of São Carlos - UFScar
dc.description.affiliationMedizinische Klinik III Klinikum Augsburg
dc.description.affiliationRegensburg Medical Image Computing (ReMIC) Ostbayerische Technische Hochschule Regensburg - OTH Regensburg
dc.description.affiliationDepartment of Computing São Paulo State University - UNESP
dc.description.affiliationUnespDepartment of Computing São Paulo State University - UNESP
dc.identifierhttp://dx.doi.org/10.1007/s00521-018-03982-0
dc.identifier.citationNeural Computing and Applications.
dc.identifier.doi10.1007/s00521-018-03982-0
dc.identifier.issn0941-0643
dc.identifier.scopus2-s2.0-85059772077
dc.identifier.urihttp://hdl.handle.net/11449/190024
dc.language.isoeng
dc.relation.ispartofNeural Computing and Applications
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectAdenocarcinoma
dc.subjectBarrett’s esophagus
dc.subjectImage processing
dc.subjectMachine learning
dc.subjectOptimum-path forest
dc.titleLearning visual representations with optimum-path forest and its applications to Barrett’s esophagus and adenocarcinoma diagnosisen
dc.typeArtigo
unesp.author.orcid0000-0002-6494-7514[9]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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