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A weighted voting ensemble self-labeled algorithm for the detection of lung abnormalities from X-rays. (English) Zbl 1461.92049

Summary: During the last decades, intensive efforts have been devoted to the extraction of useful knowledge from large volumes of medical data employing advanced machine learning and data mining techniques. Advances in digital chest radiography have enabled research and medical centers to accumulate large repositories of classified (labeled) images and mostly of unclassified (unlabeled) images from human experts. Machine learning methods such as semi-supervised learning algorithms have been proposed as a new direction to address the problem of shortage of available labeled data, by exploiting the explicit classification information of labeled data with the information hidden in the unlabeled data. In the present work, we propose a new ensemble semi-supervised learning algorithm for the classification of lung abnormalities from chest X-rays based on a new weighted voting scheme. The proposed algorithm assigns a vector of weights on each component classifier of the ensemble based on its accuracy on each class. Our numerical experiments illustrate the efficiency of the proposed ensemble methodology against other state-of-the-art classification methods.

MSC:

92C55 Biomedical imaging and signal processing
68T10 Pattern recognition, speech recognition

Software:

C4.5; WEKA
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References:

[1] Van Ginneken, B.; Stegmann, M.B.; Loog, M.; Segmentation of anatomical structures in chest radiographs using supervised methods: A comparative study on a public database; Medical Image Anal.: 2006; Volume 10 ,19-40.
[2] Livieris, I.; Kanavos, A.; Tampakas, V.; Pintelas, P.; An ensemble SSL algorithm for efficient chest X-ray image classification; J. Imaging: 2018; Volume 4 . · Zbl 1461.68187
[3] Zhu, X.; Goldberg, A.; Introduction to semi-supervised learning; Synth. Lect. Artif. Intell. Mach. Learn.: 2009; Volume 3 ,1-130. · Zbl 1209.68435
[4] Chapelle, O.; Scholkopf, B.; Zien, A.; Semi-supervised learning; IEEE Trans. Neural Netw.: 2009; Volume 20 ,542.
[5] Levatic, J.; Dzeroski, S.; Supek, F.; Smuc, T.; Semi-supervised learning for quantitative structure-activity modeling; Informatica: 2013; Volume 37 ,173-179.
[6] Levatić, J.; Ceci, M.; Kocev, D.; Džeroski, S.; Semi-supervised classification trees; J. Intell. Inf. Syst.: 2017; Volume 49 ,461-486.
[7] Livieris, I.; Kanavos, A.; Tampakas, V.; Pintelas, P.; An auto-adjustable semi-supervised self-training algorithm; Algorithm: 2018; Volume 11 . · Zbl 1461.68187
[8] Livieris, I.; Kiriakidou, N.; Kanavos, A.; Tampakas, V.; Pintelas, P.; On ensemble SSL algorithms for credit scoring problem; Informatics: 2018; Volume 5 .
[9] Triguero, I.; García, S.; Herrera, F.; Self-labeled techniques for semi-supervised learning: Taxonomy, software and empirical study; Knowl. Inf. Syst.: 2015; Volume 42 ,245-284.
[10] Yarowsky, D.; Unsupervised word sense disambiguation rivaling supervised methods; Proceedings of the 33rd Annual Meeting of the Association For Computational Linguistics: ; ,189-196.
[11] Blum, A.; Mitchell, T.; Combining labeled and unlabeled data with co-training; Proceedings of the 11th Annual Conference on Computational Learning Theory: ; ,92-100.
[12] Zhou, Y.; Goldman, S.; Democratic co-learning; Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI): Piscataway, NI, USA 2004; ,594-602.
[13] Zhou, Z.; Li, M.; Tri-training: Exploiting unlabeled data using three classifiers; IEEE Trans. Knowl. Data Eng.: 2005; Volume 17 ,1529-1541.
[14] Li, M.; Zhou, Z.; Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples; IEEE Trans. Syst. Man Cybern. Part A Syst. Hum.: 2007; Volume 37 ,1088-1098.
[15] Hady, M.; Schwenker, F.; Combining committee-based semi-supervised learning and active learning; J. Comput. Sci. Technol.: 2010; Volume 25 ,681-698.
[16] Livieris, I.; Kotsilieris, T.; Anagnostopoulos, I.; Tampakas, V.; DTCo: An ensemble SSL algorithm for X-rays classification; Advances in Experimental Medicine and Biology: Berlin/Heidelberg, Germany 2018; .
[17] Livieris, I.; Kanavos, A.; Pintelas, P.; Detecting lung abnormalities from X-rays using and improved SSL algorithm; Electron. Notes Theor. Comput. Sci.: 2019; . · Zbl 1461.92049
[18] Livieris, I.; A new ensemble self-labeled semi-supervised algorithm; Informatica: 2018; .
[19] Jaeger, S.; Karargyris, A.; Candemir, S.; Folio, L.; Siegelman, J.; Callaghan, F.; Xue, Z.; Palaniappan, K.; Singh, R.; Antani, S.; Automatic tuberculosis screening using chest radiographs; IEEE Trans. Med. Imaging: 2014; Volume 33 ,233-245.
[20] Melendez, J.; van Ginneken, B.; Maduskar, P.; Philipsen, R.; Reither, K.; Breuninger, M.; Adetifa, I.; Maane, R.; Ayles, H.; Sánchez, C.; A novel multiple-instance learning-based approach to computer-aided detection of tuberculosis on chest X-rays; IEEE Trans. Med. Imaging: 2015; Volume 34 ,179-192.
[21] Alam, J.; Alam, S.; Hossan, A.; Multi-Stage Lung Cancer Detection and Prediction Using Multi-class SVM Classifier; Proceedings of the 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering: Piscataway, NI, USA 2018; ,1-4.
[22] Madani, A.; Moradi, M.; Karargyris, A.; Syeda-Mahmood, T.; Semi-supervised learning with generative adversarial networks for chest X-ray classification with ability of data domain adaptation; Proceedings of the 15th IEEE International Symposium on Biomedical Imaging: Piscataway, NI, USA 2018; ,1038-1042.
[23] Guan, Q.; Huang, Y.; Multi-label chest X-ray image classification via category-wise residual attention learning; Pattern Recognit. Lett.: 2018; .
[24] Dietterich, T.; Ensemble methods in machine learning; Multiple Classifier Systems: Berlin/Heidelberg, Germany 2001; Volume Volume 1857 ,1-15.
[25] Rokach, L.; ; Pattern Classification Using Ensemble Methods: Singapore 2010; . · Zbl 1187.68495
[26] Powers, D.M.; Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation; J. Mach. Learn. Technol.: 2011; Volume 2 ,37-63.
[27] Hall, M.; Frank, E.; Holmes, G.; Pfahringer, B.; Reutemann, P.; Witten, I.; The WEKA data mining software: An update; SIGKDD Explor. Newsl.: 2009; Volume 11 ,10-18.
[28] Kermany, D.; Goldbaum, M.; Cai, W.; Valentim, C.; Liang, H.; Baxter, S.; McKeown, A.; Yang, G.; Wu, X.; Yan, F.; Identifying medical diagnoses and treatable diseases by image-based deep learning; Cell: 2018; Volume 172 ,1122-1131.
[29] Stirenko, S.; Kochura, Y.; Alienin, O.; Rokovyi, O.; Gang, P.; Zeng, W.; Gordienko, Y.; Chest X-ray analysis of tuberculosis by deep learning with segmentation and augmentation; arXiv: 2018; .
[30] Albertina, B.; Watson, M.; Holback, C.; Jarosz, R.; Kirk, S.; Lee, Y.; Lemmerman, J.; Radiology data from the cancer Genome Atlas Lung Adenocarcinoma [TCGA-LUAD] collection; Cancer Imaging Arch.: 2016; .
[31] Clark, K.; Vendt, B.; Smith, K.; Freymann, J.; Kirby, J.; Koppel, P.; Moore, S.; Phillips, S.; Maffitt, D.; Pringle, M.; The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository; J. Digit. Imaging: 2013; Volume 26 ,1045-1057.
[32] Wang, Y.; Xu, X.; Zhao, H.; Hua, Z.; Semi-supervised learning based on nearest neighbor rule and cut edges; Knowl.-Based Syst.: 2010; Volume 23 ,547-554.
[33] Platt, J.; ; Advances in Kernel Methods—Support Vector Learning: Cambridge, MA, USA 1998; .
[34] Quinlan, J.; ; C4.5: Programs for Machine Learning: San Francisco, CA, USA 1993; .
[35] Aha, D.; ; Lazy Learning: Dordrecht, The Netherlands 1997; . · Zbl 0869.68071
[36] Wu, X.; Kumar, V.; Quinlan, J.; Ghosh, J.; Yang, Q.; Motoda, H.; McLachlan, G.; Ng, A.; Liu, B.; Yu, P.; Top 10 algorithms in data mining; Knowl. Inf. Syst.: 2008; Volume 14 ,1-37.
[37] Hodges, J.; Lehmann, E.; Rank methods for combination of independent experiments in analysis of variance; Ann. Math. Stat.: 1962; Volume 33 ,482-497. · Zbl 0112.10303
[38] Finner, H.; On a monotonicity problem in step-down multiple test procedures; J. Am. Stat. Assoc.: 1993; Volume 88 ,920-923. · Zbl 0799.62077
[39] Li, S.; Wang, Z.; Zhou, G.; Lee, S.; Semi-supervised learning for imbalanced sentiment classification; Proceedings of the IJCAI Proceedings-International Joint Conference on Artificial Intelligence: ; Volume Volume 22 ,1826.
[40] Jeni, L.A.; Cohn, J.F.; De La Torre, F.; Facing imbalanced data—Recommendations for the use of performance metrics; Proceedings of the Humaine Association Conference on Affective Computing and Intelligent Interaction: Piscataway, NI, USA 2013; ,245-251.
[41] Levatić, J.; Ceci, M.; Kocev, D.; Džeroski, S.; Self-training for multi-target regression with tree ensembles; Knowl.-Based Syst.: 2017; Volume 123 ,41-60.
[42] Levatić, J.; Kocev, D.; Džeroski, S.; The importance of the label hierarchy in hierarchical multi-label classification; J. Intell. Inf. Syst.: 2015; Volume 45 ,247-271.
[43] Levatić, J.; Kocev, D.; Ceci, M.; Džeroski, S.; Semi-supervised trees for multi-target regression; Inf. Sci.: 2018; Volume 450 ,109-127.
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