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Multi-Phase Feature Representation Learning for Neurodegenerative Disease Diagnosis

1BMIT Research Group, School of Information Technologies, University of Sydney, NSW, Australia.
2Surgical Planning Laboratory, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, USA.
Publication Date:
ACALCI 2015 Feb; LNAI 8955:350-9, 2015.
classification, deep learning, neuroimaging
Appears in Collections:
P41 EB015902/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Liu S., Liu S., Cai W., Pujol S., Kikinis R., Feng D.D. Multi-Phase Feature Representation Learning for Neurodegenerative Disease Diagnosis. ACALCI 2015 Feb; LNAI 8955:350-9, 2015.
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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 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 low-dimensional biomarkers 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 highdimensional 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.

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