Multi-Phase Feature Representation Learning for Neurodegenerative Disease Diagnosis [chapter]

Siqi Liu, Sidong Liu, Weidong Cai, Sonia Pujol, Ron Kikinis, David Dagan Feng
2015 Lecture Notes in Computer Science  
Feature learning with high dimensional neuroimaging features has been explored for the applications on neurodegenerative diseases. Low-dimensional biomarkers, such as mental status test scores, gene variations, and protein changes in plasma and cerebrospinal fluid level, are essential in clinical diagnosis of neurological disorders, because they could be simple and effective for the clinicians to assess the disorder's progression and severity. Rather than only using the lowdimensional
more » ... as inputs for decision making systems, we believe that such low-dimensional biomarkers can be used for enhancing the feature learning pipeline. In this study, we proposed a novel feature representation learning framework, Multi-Phase Feature Representation (MPFR), with low-dimensional biomarkers embedded. MPFR learns high-level neuroimaging features by extracting the associations between the low-dimensional biomarkers and the high-dimensional neuroimaging features with a deep neural network. We validated the proposed framework using the Mini-Mental-State-Examination (MMSE) scores as a low-dimensional biomarker and multi-modal neuroimaging data as the high-dimensional neuroimaging features from the ADNI baseline cohort. The proposed approach outperformed the original neural network in both binary and ternary Alzheimer's disease classification tasks.
doi:10.1007/978-3-319-14803-8_27 fatcat:6r46rorxnfhbjdq4by4uz2xgga