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

  • Intelligent Biomedical Data Analysis and Processing
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Abstract

Considering 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.

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Notes

  1. https://endovissub-barrett.grand-challenge.org/home/.

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Acknowledgements

The authors are grateful to DFG Grant PA 1595/3-1, Capes/Alexander von Humboldt Foundation Grant No. BEX 0581-16-0, CNPq Grants 306166/2014-3 and 307066/2017-7, as well as FAPESP Grants 2013/07375-0, 2014/12236-1, and 2016/19403-6. This material is based upon work supported in part by funds provided by Intel® AI Academy program under Fundunesp Grant No. 2597.2017.

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Authors and Affiliations

  1. Department of Computing, Federal University of São Carlos - UFScar, São Carlos, Brazil

    Luis A. de Souza Jr. & Luis C. S. Afonso

  2. Medizinische Klinik III, Klinikum Augsburg, Augsburg, Germany

    Alanna Ebigbo, Andreas Probst & Helmut Messmann

  3. Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg - OTH Regensburg, Regensburg, Germany

    Robert Mendel, Christian Hook & Christoph Palm

  4. Department of Computing, São Paulo State University - UNESP, Bauru, Brazil

    João P. Papa

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  1. Luis A. de Souza Jr.
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  2. Luis C. S. Afonso
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Correspondence to João P. Papa.

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de Souza, L.A., Afonso, L.C.S., Ebigbo, A. et al. Learning visual representations with optimum-path forest and its applications to Barrett’s esophagus and adenocarcinoma diagnosis. Neural Comput & Applic 32, 759–775 (2020). https://doi.org/10.1007/s00521-018-03982-0

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