Enhancing Alzheimer's prognostic models with cross-domain self-supervised learning and MRI data harmonization

In the rapidly evolving field of medical imaging, the development of effective artificial intelligence systems requires both advanced deep learning algorithms and substantial, high-quality datasets. However, the acquisition and annotation of such data, particularly in specialized domains like clinical disease prognostics, is often prohibitively expensive and time-consuming. This research explores the potential of cross-domain self-supervised learning (CDSSL) as an innovative solution to these challenges, with a specific focus on enhancing Alzheimer's disease progression models using brain Magnetic Resonance Imaging (MRI) data. Our study introduces a novel CDSSL approach tailored for disease prognostic modeling, emphasizing regression tasks in medical imaging. Using Alzheimer's disease progression prediction from brain MRI as a case study, we demonstrate that self-supervised pretraining significantly improves prognostic accuracy. Notably, models pretrained on extended, unlabeled brain MRI datasets consistently outperform those using natural images, with an optimal combination of both data sources yielding the best results. Furthermore, we address the critical issue of data harmonization in medical imaging, investigating the impact of scanner-specific variations arising from diverse manufacturers and models. Our findings highlight CDSSL's potential in ensuring data consistency across different scanner environments, thereby enhancing data comparability and reproducibility. Specifically, we propose two methods Augmentation CDSSL and Auxiliary CDSSL, and show improved prognostic model and scanner variability reduction. Additionally, we compare our methods with an unsupervised harmonization model, demonstrating that our approach achieves better results in most of the datasets. This research underscores the significance of scanner-aware self-supervised learning in refining medical imaging methodologies, particularly in the context of Alzheimer's disease (AD) progression modeling. The proposed approach not only improves model accuracy and robustness in limited data scenarios but also offers a promising solution for mitigating scanner variability. These advancements have profound implications for the application of Artificial Intelligence (AI) in clinical settings, potentially leading to more accurate and reliable prognostic tools for AD.

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