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. 2018 Apr 9;8:5697. doi: 10.1038/s41598-018-22871-z

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Multimodal and Multiscale Deep Neural Network. The input feature dimension (number of patches) extracted from different scales is 1488, 705 and 343. For each layer, its number of nodes is shown on the top left of the layer representation. For each scale of each image modality, its patch-wise measures were fed to a single DNN. The features from these 6 DNNs were fused by another DNN to generate the final probability score for each of the two classes being discriminated. Of the two classes, the class being the one with the highest probability (effectively a threshold of 0.5 for probability) is the assigned final classification. The probability output of the DNN can be interpreted as a staging score, with extreme value of 0 representing the highest probability of belonging to the sNC class, and extreme value of 1 representing the highest probability of belonging to the AD class.