A blockchain and decentralized storage technology-based architecture is proposed to support the integrity of Electronic Health Records (EHRs).
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| http://dx.doi.org/10.3233/SHTI230801 | DOI Listing |
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Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia.
This study presents a novel privacy-preserving self-supervised (SSL) framework for COVID-19 classification from lung CT scans, utilizing federated learning (FL) enhanced with Paillier homomorphic encryption (PHE) to prevent third-party attacks during training. The FL-SSL based framework employs two publicly available lung CT scan datasets which are considered as labeled and an unlabeled dataset. The unlabeled dataset is split into three subsets which are assumed to be collected from three hospitals.
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In the rapidly evolving healthcare sector, using advanced technologies to improve medical classification systems has become crucial for enhancing patient care, diagnosis, and treatment planning. There are two main challenges faced in this domain (i) imbalanced distribution of medical data, leading to biased model performance and (ii) the need to preserve patient privacy and comply with data protection regulations. The primary goal of this project is to develop a medical classification model for Alzheimer's disease detection that can effectively learn from decentralized and imbalanced datasets without compromising on data privacy.
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