This README file describes the models proposed in the current folder. The models were created using clinicadl == 1.0.4. Each folder containing the models is compressed in a tar.gz file. The filename corresponds to the experiments described in the supplementary material of the main publication [1], see the eTable 4. Here a simplified version of the aforementioned table: | Experiment | Architecture | Training Data | Transfer learning | Task | | 3 | 3D subject-level CNN | Baseline | AE | AD vs CN | | 8 | 3D roi-based CNN | Baseline | AE | AD vs CN | | 14 | 3D patch-level CNN | Baseline | AE | AD vs CN | | 18 | 2D slice-level CNN | Baseline | ImageNet pretrain | AD vs CN | Model architecture, weights and hyperparameters are self-contained in each folder and are organized by followint the MAPS structure [2]. [1] Junhao Wen, Elina Thibeau-Sutre, Mauricio Diaz-Melo, Jorge Samper-González, Alexandre Routier, Simona Bottani, Didier Dormont, Stanley Durrleman, Ninon Burgos, Olivier Colliot, Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation, Medical Image Analysis, Volume 63, 2020, 101694, ISSN 1361-8415. [2] https://clinicadl.readthedocs.io/en/stable/Introduction/#maps-definition @Copyright 2020-2022, Aramislab, Inria.