Semi-supervised learning for mRALE score prediction in COVID-19 chest radiographs

Christian Mattjie; Luis V. de Moura; Rafaela C. Ravazio; Otavio Parraga; Lucas S. Kupssinskü; Adilson Medronha; Ana M. M. da Silva; Rodrigo C. Barros

doi:10.1117/12.3003750

3 April 2024 Semi-supervised learning for mRALE score prediction in COVID-19 chest radiographs

Christian Mattjie, Luis V. de Moura, Rafaela C. Ravazio, Otavio Parraga, Lucas S. Kupssinskü, Adilson Medronha, Ana M. M. da Silva, Rodrigo C. Barros

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 1292709 (2024) https://doi.org/10.1117/12.3003750
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

This study presents a semi-supervised learning strategy to predict Modified Radiographic Assessment of Lung Edema (mRALE) scores from chest radiographs (CXR) of COVID-19 patients. Using a combination of EfficientNet models and large volumes of unlabeled data, we developed a robust deep-learning model that exhibits outstanding performance, securing 4th place in the MIDRC mRALE Mastermind Challenge. Our strategy harnessed both the labeled data provided in the challenge and a significant amount of unlabeled data from the Medical Imaging and Data Resource Center (MIDRC) and CheXpert. We used labeled data to train an EfficientNet-B4 model, which we used to infer pseudo labels on our unlabeled data. We then used Chexpert to pre-train an EfficientNet-B7 model from scratch, allowing it to extract valuable information from a broader scope of CXR images. The final step involved fine-tuning the pre-trained EfficientNet-B7 model using additional CXR images from MIDRC. This approach effectively adapted the model to our specific domain of interest: portable CXR images from COVID-19 patients. The success of this semi-supervised learning approach highlights its potential to enhance diagnostic capabilities in medical imaging, particularly in situations where labeled data is limited. It demonstrates the feasibility of leveraging massive volumes of unlabeled data, even when the specific disease differs, provided that radiological characteristics are similar. This study paves the way for advancements in deep learning applications in healthcare, especially in emergency scenarios that demand swift and accurate diagnostics, such as the COVID-19 pandemic.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Christian Mattjie, Luis V. de Moura, Rafaela C. Ravazio, Otavio Parraga, Lucas S. Kupssinskü, Adilson Medronha, Ana M. M. da Silva, and Rodrigo C. Barros "Semi-supervised learning for mRALE score prediction in COVID-19 chest radiographs", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292709 (3 April 2024); https://doi.org/10.1117/12.3003750

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