doi:10.1109/ISBI45749.2020.9098445engRamón-Júlvez, UbaldoHernández, MónicaMayordomo, ElviraADNIAnalysis of the influence of diffeomorphic normalization in the prediction of stable VS progressive MCI conversion with convolutional neural networksART-2020-118591We study the effect of the selection of diffeomorphic normalization in the performance of Spasov''s deep-learning system for the problem of progressive MCI vs stable MCI discrimination. We considered different degrees of normalization (no, affine and non-rigid normalization) and two diffeomorphic registration methods (ANTS and BL PDE-LDDMM) with different image similarity metrics (SSD, NCC, and lNCC) yielding qualitatively different deformation models and quantitatively different degrees of registration accuracy. BL PDE-LDDMM NCC achieved the best performing accuracy with median values of 89%. Surprisingly, the accuracy of no and affine normalization was also among the highest, indicating that the deep-learning system is powerful enough to learn accurate models for pMCI vs sMCI discrimination without the need for normalization. However, the best sensitivity values were obtained by BL PDE-LDDMM SSD and NCC with median values of 97% and 94% while the sensitivity of the remaining methods stayed under 88%.2020http://zaguan.unizar.es/record/13129310.1109/ISBI45749.2020.9098445http://zaguan.unizar.es/record/131293oai:zaguan.unizar.es:131293info:eu-repo/grantAgreement/ES/MICINN/TIN2016-80347-RProceedings - International Symposium on Biomedical Imaging 2020-April (2020), 1120-1124All rights reservedhttp://www.europeana.eu/rights/rr-f/info:eu-repo/semantics/openAccess