Encoding and decoding multimodal brain images for disease biomarker discovery - Chan, Yi Hao (Nanyang Technological University, 2024)
Many neurodegenerative diseases (such as Alzheimer’s disease) and neuropsychiatric disorders (such as schizophrenia) are still poorly understood. While incorporating multimodal datasets (structural and functional) would provide a more complete picture of these neurological conditions, existing studies already struggle with two key challenges even when a single data modality is used: data scarcity and high dimensionality. These problems make machine learning models overfit easily on neuroimaging datasets and result in the generation of spurious biomarkers. Coupled with issues such as inter-scanner variability and disease heterogeneity, biomarker discovery remains an onerous task as reproducible biomarkers for these neurological conditions have proven to be elusive. Overcoming these obstacles would give us a better understanding of these diseases and possibly lead to the discovery of new cures. One emerging trend in the area of biomarker discovery is the use of deep learning models. Despite its perceived complexity, one relatively unexplored aspect of deep learning architectures is the flexibility they afford for modelling multimodal datasets, in terms of architectural design and construction of the loss function. In this thesis, we propose several deep learning architectures customised for the idiosyncrasies of brain imaging datasets and demonstrate how they can produce reliable biomarkers.