http://www.nitrc.org/forum/forum.php?forum_id=6132 <table border="0" width="100%"><tr><td align="left"/></tr></table> <p><b>A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI.</b></p> <p>Med Image Comput Comput Assist Interv. 2015 Oct;9349:573-580</p> <p>Authors: Suk HI, Lee SW, Shen D</p> <p>Abstract<br/> In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inherent in rs-fMRI, i.e., internal state changes. By building a generative model with HMMs for each class individually, we estimate the data likelihood of a test subject as MCI or normal healthy control, based on which we identify the clinical label. In our experiments, we achieved the maximal accuracy of 81.08% with the proposed method, outperforming state-of-the-art methods in the literature.<br/> </p><p>PMID: 27054199 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Mon, 04 May 2026 22:12:55 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator