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Authors: Felipe Zeiser 1 ; Ismael Santos 1 ; Henrique Bohn 1 ; Cristiano André da Costa 1 ; Gabriel Ramos 1 ; Rodrigo da Rosa Righi 1 ; Andreas Maier 2 ; José Andrade 3 and Alexandre Bacelar 3

Affiliations: 1 Software Innovation Laboratory - SOFTWARELAB, Universidade do Vale do Rio dos Sinos - Unisinos, São Leopoldo, Brazil ; 2 Department of Computer Science, Friedrich-Alexander-Universität, Erlangen-Nürnberg, Erlangen, Germany ; 3 Medical Physics and Radioprotection Service, Hospital de Clinicas de Porto Alegre, Porto Alegre, Brazil

Keyword(s): Contrastive Learning, X-Ray, Pleural Effusion.

Abstract: Diagnosing pleural effusion is important to recognize the disease’s etiology and reduce the length of hospital stay for patients after fluid content analysis. In this context, machine learning techniques have been increasingly used to help physicians identify radiological findings. In this work, we propose using contrastive learning to classify chest X-rays with and without pleural effusion. A model based on contrastive learning is trained to extract discriminative features from the images and reports to maximize the similarity between the correct image and text pairs. Preliminary results show that the proposed approach is promising, achieving an AUC of 0.900, an accuracy of 86.28%, and a sensitivity of 88.54% for classifying pleural effusion on chest X-rays. These results demonstrate that the proposed method achieves comparable or superior to state of the art results. Using contrastive learning can be a promising alternative to improve the accuracy of medical image classification mo dels, contributing to a more accurate and effective diagnosis. (More)

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Paper citation in several formats:
Zeiser, F.; Santos, I.; Bohn, H.; André da Costa, C.; Ramos, G.; da Rosa Righi, R.; Maier, A.; Andrade, J. and Bacelar, A. (2023). Pleural Effusion Classification on Chest X-Ray Images with Contrastive Learning. In Proceedings of the 19th International Conference on Web Information Systems and Technologies - WEBIST; ISBN 978-989-758-672-9; ISSN 2184-3252, SciTePress, pages 399-405. DOI: 10.5220/0012205900003584

@conference{webist23,
author={Felipe Zeiser. and Ismael Santos. and Henrique Bohn. and Cristiano {André da Costa}. and Gabriel Ramos. and Rodrigo {da Rosa Righi}. and Andreas Maier. and José Andrade. and Alexandre Bacelar.},
title={Pleural Effusion Classification on Chest X-Ray Images with Contrastive Learning},
booktitle={Proceedings of the 19th International Conference on Web Information Systems and Technologies - WEBIST},
year={2023},
pages={399-405},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012205900003584},
isbn={978-989-758-672-9},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Web Information Systems and Technologies - WEBIST
TI - Pleural Effusion Classification on Chest X-Ray Images with Contrastive Learning
SN - 978-989-758-672-9
IS - 2184-3252
AU - Zeiser, F.
AU - Santos, I.
AU - Bohn, H.
AU - André da Costa, C.
AU - Ramos, G.
AU - da Rosa Righi, R.
AU - Maier, A.
AU - Andrade, J.
AU - Bacelar, A.
PY - 2023
SP - 399
EP - 405
DO - 10.5220/0012205900003584
PB - SciTePress

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