Pleural Effusion Classification on Chest X-Ray Images with Contrastive Learning

Felipe Zeiser, Ismael Santos, Henrique Bohn, Cristiano André da Costa, Gabriel Ramos, Rodrigo da Rosa Righi, Andreas Maier, José Andrade, Alexandre Bacelar

2023

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 models, contributing to a more accurate and effective diagnosis.

Paper Citation

in Harvard Style

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 - Volume 1: WEBIST; ISBN 978-989-758-672-9, SciTePress, pages 399-405. DOI: 10.5220/0012205900003584

in Bibtex Style

@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 - Volume 1: WEBIST},
year={2023},
pages={399-405},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012205900003584},
isbn={978-989-758-672-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST
TI - Pleural Effusion Classification on Chest X-Ray Images with Contrastive Learning
SN - 978-989-758-672-9
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