End-to-end privacy preserving deep learning on multi-institutional medical imaging

Abstract

he rapid evolution of artificial intelligence (AI) and machine learning (ML) in biomedical data analysis has recently yielded encouraging results, showcasing AI systems able to assist clinicians in a variety of scenarios, such as the early detection of cancers in medical imaging 1,2 . Such systems are maturing past the proof-of-concept stage and are expected to reach widespread application in the coming years as witnessed by rising numbers of patent applications 3 and regulatory approvals 4 . The common denominator of high-performance AI systems is the requirement for large and diverse datasets for training the ML models, often achieved by voluntary data sharing on behalf of the data owners and multi-institutional or multi-national dataset accumulation. It's common for patient data to be anonymized or pseudonymized at the originating institution, then transmitted to and stored at the site of analysis and model training (known as centralized data sharing) 5 . However, anonymization has proven to provide insufficient protection against re-identification attacks Therefore, large-scale collection, aggregation and transmission of patient data is critical from a legal and an ethical viewpoint 8 . Furthermore, it is a fundamental patient right to be in control of the storage, transmission and usage of personal health data. Centralized data sharing practically eliminates this control, leading to a loss of sovereignty. Moreover, anonymized data, once transmitted, cannot easily be retrospectively corrected or augmented, for example by introducing additional clinical information that becomes available.

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