Adapt innovative deep learning methods from breast cancer to Alzheimers disease

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Adapt innovative deep learning methods from breast cancer to Alzheimer’s disease Abstract Alzheimer’s disease (AD) is the most common form of dementia, with the number of affected Americans expected to reach 13.4 million by the year 2050. Early detection and treatment of AD is critical to prevent non- reversible and fatal brain damage. Thus, development of non-invasive markers from neuroimaging modalities (e.g., brain MRI) is of great significance for screening and early detection of AD. In the PI’s active R01 award (1R01EB032896-01), the research focuses on developing a new line of research strategy and technical innovation to analyze breast cancer images for diagnosis, risk prediction, and triage. The core strategy is to incorporate medical/clinical intelligence into data-driven deep learning modeling. This technical innovation is however not limited to breast cancer, but can be adapted to other diseases, such as AD, as well. Thus, in this Supplement proposal, we propose to develop an AD focus of our active R01 by adapting the new technical innovation in breast cancer into cognitive outcome prediction for discovering early and no-invasive imaging biomarkers for AD. The main task of this Supplement study is to build deep learning models using brain MRIs as input for cognitive outcome prediction, which is formulated as a typical classification problem among three cognitive classes: Normal Control vs. Mild Cognitive Impairment vs. AD. We proposed two specific aims: 1) Deep curriculum learning informed by samples’ characteristic knowledge for cognitive outcome prediction and 2) Learning knowledge from longitudinal brain MRIs to improve prediction of AD. We will mainly use the publicly available Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. We have assembled a multi- disciplinary team with complementary expertise. The proposed study will provide an avenue to translate some of the innovative techniques developed in other domains to advance non-invasive imaging biomarker development for AD. This project will also provide an opportunity for the PI’s team to get involved and contribute to AD-related new research.