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