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Poster De Conférence Année : 2021

Explaining 3D CNNs for Alzheimer’s Disease Classification on sMRI Images with Multiple ROIs

Résumé

Classification of Alzheimer’s disease from 3D structural Magnetic Resonance Imaging (sMRI) with deep neural networks has shown promising results in recent years. The decision interpretation of these networks is essential to aid medical experts to understand and rely on the results provided by such models. In this paper, we propose an adaptation of a recently developed feature-based explanation method and apply it to a 3D CNN architecture for the binary classification of Alzheimer’s disease and Normal Control from the hippocampal ROIs of brain sMRIs. We also compare our method to the state-of-the-art LRP method.
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Dates et versions

hal-04098155 , version 1 (15-05-2023)

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Meghna Ayyar, Jenny Benois-Pineau, Akka Zemmari, Gwenaelle Catheline. Explaining 3D CNNs for Alzheimer’s Disease Classification on sMRI Images with Multiple ROIs. 2021 IEEE International Conference on Image Processing (ICIP), Sep 2021, Anchorage, United States. IEEE, pp.284-288, ⟨10.1109/ICIP42928.2021.9506472⟩. ⟨hal-04098155⟩

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