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Robust Deep Learning for Improved Classification of AD/MCI Patients

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Machine Learning in Medical Imaging (MLMI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8679))

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Accurate classification of Alzheimer’s Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), plays a critical role in preventing progression of memory impairment and improving quality of life for AD patients. Among many research tasks, it is of particular interest to identify noninvasive imaging biomarkers for AD diagnosis. In this paper, we present a robust deep learning system to identify different progression stages of AD patients based on MRI and PET scans. We utilized the dropout technique to improve classical deep learning by preventing its weight co-adaptation, which is a typical cause of over-fitting in deep learning. In addition, we incorporated stability selection, an adaptive learning factor and a multi-task learning strategy into the deep learning framework. We applied the proposed method to the ADNI data set and conducted experiments for AD and MCI conversion diagnosis. Experimental results showed that the dropout technique is very effective in AD diagnosis, improving the classification accuracies by 6.2% on average as compared to classical deep learning methods.

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Li, F., Tran, L., Thung, KH., Ji, S., Shen, D., Li, J. (2014). Robust Deep Learning for Improved Classification of AD/MCI Patients. In: Wu, G., Zhang, D., Zhou, L. (eds) Machine Learning in Medical Imaging. MLMI 2014. Lecture Notes in Computer Science, vol 8679. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10580-2

  • Online ISBN: 978-3-319-10581-9

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