Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks

Pattern recognition methods using neuroimaging data for the diagnosis of Alzheimer's disease have been the subject of extensive research in recent years. In this paper, we use deep learning methods, and in particular sparse autoencoders and 3D convolutional neural networks, to build an algorithm that can predict the disease status of a patient, based on an MRI scan of the brain. We report on experiments using the ADNI data set involving 2,265 historical scans. We demonstrate that 3D convolutional neural networks outperform several other classifiers reported in the literature and produce state-of-art results.

[1]  Marc'Aurelio Ranzato,et al.  Efficient Learning of Sparse Representations with an Energy-Based Model , 2006, NIPS.

[2]  Nick C Fox,et al.  Accuracy of dementia diagnosis—a direct comparison between radiologists and a computerized method , 2008, Brain : a journal of neurology.

[3]  Yann LeCun,et al.  What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[4]  Ben Taskar,et al.  A General and Unifying Framework for Feature Construction, in Image-Based Pattern Classification , 2009, IPMI.

[5]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[6]  T. Chan,et al.  Independent component analysis-based classification of Alzheimer's disease MRI data. , 2011, Journal of Alzheimer's disease : JAD.

[7]  Yoshua Bengio,et al.  Practical Recommendations for Gradient-Based Training of Deep Architectures , 2012, Neural Networks: Tricks of the Trade.

[8]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[9]  Adni,et al.  Biomarker discovery for sparse classification of brain images in Alzheimer's disease , 2012 .

[10]  W. Thies,et al.  2013 Alzheimer's disease facts and figures , 2013, Alzheimer's & Dementia.

[11]  Anthony Maida,et al.  Natural Image Bases to Represent Neuroimaging Data , 2013, ICML.

[12]  Sidong Liu,et al.  Early diagnosis of Alzheimer's disease with deep learning , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[13]  Daniel Rueckert,et al.  Multiple instance learning for classification of dementia in brain MRI , 2013, Medical Image Anal..