CAFES: chest x-ray analysis using federated self-supervised learning for pediatric Covid-19 detection

Abhijeet Parida; Syed Muhammad Anwar; Malhar P. Patel; Mathias Blom; Tal Tiano Einat; Alex Tonetti; Yuval Baror; Ittai Dayan; Marius George Linguraru

doi:10.1117/12.3008757

3 April 2024 CAFES: chest x-ray analysis using federated self-supervised learning for pediatric Covid-19 detection

Abhijeet Parida, Syed Muhammad Anwar, Malhar P. Patel, Mathias Blom, Tal Tiano Einat, Alex Tonetti, Yuval Baror, Ittai Dayan, Marius George Linguraru

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129271I (2024) https://doi.org/10.1117/12.3008757
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Chest X-rays (CXRs) play a pivotal role in cost-effective clinical assessment of various heart and lung related conditions. The urgency of COVID-19 diagnosis prompted their use in identifying conditions like lung opacity, pneumonia, and acute respiratory distress syndrome in pediatric patients. We propose an AI-driven solution for binary COVID-19 versus non-COVID-19 classification in pediatric CXRs. We present a Federated Self-Supervised Learning (FSSL) framework to enhance Vision Transformer (ViT) performance for COVID-19 detection in pediatric CXRs. ViT's prowess in vision-related binary classification tasks, combined with self-supervised pre-training on adult CXR data, forms the basis of the FSSL approach. We implement our strategy on the Rhino Health Federated Computing Platform (FCP), which ensures privacy and scalability for distributed data. The chest X-ray analysis using the federated SSL (CAFES) model, utilizes the FSSL-pre-trained ViT weights and demonstrated gains in accurately detecting COVID-19 when compared with a fully supervised model. Our FSSL-pre-trained ViT showed an area under the precision-recall curve (AUPR) of 0.952, which is 0.231 points higher than the fully supervised model for COVID-19 diagnosis using pediatric data. Our contributions include leveraging vision transformers for effective COVID-19 diagnosis from pediatric CXRs, employing distributed federated learning-based self-supervised pre-training on adult data, and improving pediatric COVID-19 diagnosis performance. This privacy-conscious approach aligns with HIPAA guidelines, paving the way for broader medical imaging applications.

Conference Presentation

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

Citation Download Citation

Abhijeet Parida, Syed Muhammad Anwar, Malhar P. Patel, Mathias Blom, Tal Tiano Einat, Alex Tonetti, Yuval Baror, Ittai Dayan, and Marius George Linguraru "CAFES: chest x-ray analysis using federated self-supervised learning for pediatric Covid-19 detection", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129271I (3 April 2024); https://doi.org/10.1117/12.3008757

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