Browsing by Author "Souza De Andrade, Raissa Cristina"

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    Distributed Deep Learning Methods for Medical Imaging Analysis
    (2024-10-29) Souza De Andrade, Raissa Cristina; Forkert, Nils Daniel; Wilms, Matthias; Pike, G. Bruce; Barker, Ken
    Recent advancements in deep learning have equipped healthcare professionals with valuable tools to support clinical decision-making and reduce workloads. However, many medical centers lack sufficient datasets to train deep learning models, especially for rare diseases or centers in remote or underserved areas. Although collecting and curating datasets from multiple centers into a centralized repository is commonly employed to solve this problem, this approach is often infeasible due to its costs and privacy regulations that prohibit data sharing. Consequently, many centers and populations will not benefit from cutting-edge artificial intelligence. The distributed deep learning framework proposed in this work addresses these challenges by training accurate models while patient data remains securely stored within its center. Thus, privacy concerns are addressed while collaborative multi-center training is facilitated. A key innovation of this work is the development and evaluation of the travelling model, a method well-suited for scenarios where individual centers have very limited data availability. The travelling model is evaluated across various scenarios, including extreme cases where centers contribute only a single medical image, and is applied to critical medical imaging tasks such as brain age prediction, disease classification, and tumour segmentation. In general, the travelling model effectively increases the overall dataset quantity and diversity without compromising patient data privacy. However, solutions for the inherent acquisition shift biases caused by variations in equipment and protocols across centers and decentralized data quality control are needed to leverage its full potential. Therefore, this work also developed and integrated two novel methods into the travelling model approach, a data harmonization for reducing acquisition shift biases and automated decentralized data quality control. The results of this work demonstrate that the travelling model framework achieved performances comparable to models trained on a centralized repository across all evaluated tasks. Moreover, it performed better than the commonly used federated learning in cases where centers contributed fewer than five datasets. Additionally, the proposed data harmonization method reduced scanner variability by 23%, improving disease classification accuracy by 4%. Finally, the automated decentralized quality control method effectively identified incorrect and low-quality datasets, enabling more robust and reliable model performance.