A more interpretable classifier for multiple sclerosis V Wargnier-Dauchelle, T Grenier, F Durand-Dubief, F Cotton, M Sdika 2021 IEEE 18th international symposium on biomedical imaging (ISBI), 1062-1066, 2021 | 16 | 2021 |
A weakly supervised gradient attribution constraint for interpretable classification and anomaly detection V Wargnier-Dauchelle, T Grenier, F Durand-Dubief, F Cotton, M Sdika IEEE Transactions on Medical Imaging 42 (11), 3336-3347, 2023 | 14 | 2023 |
Reproducibility of tumor segmentation outcomes with a deep learning model M Des Ligneris, A Bonnet, Y Chatelain, T Glatard, M Sdika, G Vila, ... 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 1-5, 2023 | 5 | 2023 |
Robust unsupervised image to template registration without image similarity loss S Hachicha, C Le, V Wargnier-Dauchelle, M Sdika Workshop on Medical Image Learning with Limited and Noisy Data, 148-157, 2023 | 3 | 2023 |
Saliency Maps of Video-colonoscopy Images for the Analysis of Their Content and the Prevention of Colorectal Cancer Risks. V Wargnier-Dauchelle, CS Chane, A Histace BIOSIGNALS, 106-114, 2020 | 3 | 2020 |
Constrained non-negative networks for a more explainable and interpretable classification V Wargnier-Dauchelle, T Grenier, F Durand-Dubief, F Cotton, M Sdika Medical Imaging with Deep Learning, 2024 | 2 | 2024 |
Retinal Blood Vessels Segmentation: Improving State-of-the-Art Deep Methods V Wargnier-Dauchelle, C Simon-Chane, A Histace Computer Analysis of Images and Patterns: CAIP 2019 International Workshops …, 2019 | 2 | 2019 |
An unexpected confounder: how brain shape can be used to classify MRI scans? V Wargnier-Dauchelle, T Grenier, M Sdika Medical Imaging with Deep Learning, 2024 | 1 | 2024 |
Une contrainte faiblement supervisée sur les attributions basées sur le gradient pour une classification interprétable et la détection d'anomalies V Wargnier-Dauchelle, T Grenier, F Durand-Dubief, F Cotton, M Sdika Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale (IABM …, 2023 | 1 | 2023 |
Un classifieur plus interprétable pour la sep V Wargnier-Dauchelle, T Grenier, F Durand-Dubief, F Cotton, M Sdika Congrès Société Française de Résonance Magnétique en Biologie et Médecine …, 2021 | 1 | 2021 |
Explainable monotonic networks and constrained learning for interpretable classification and weakly supervised anomaly detection V Wargnier-Dauchelle, T Grenier, F Durand-Dubief, F Cotton, M Sdika Pattern Recognition 160, 111186, 2025 | | 2025 |
Towards Better Interpretability of Sepsis Prediction by Deep Neural Networks with Variable-wise Attribution Maps PE Thiboud, V Wargnier-Dauchelle, M Lefort, N Duchateau, M Sdika | | 2024 |
Construction et apprentissage sous contraintes de réseaux monotones pour une classification interprétable et la détection d'anomalies V Wargnier-Dauchelle, T Grenier, F Durand-Dubief, F Cotton, M Sdika Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale, 2024 | | 2024 |
Interprétabilité des réseaux de neurones profonds et segmentation faiblement supervisée des lésions cérébrales sur IRM V Wargnier-Dauchelle INSA Lyon, 2023 | | 2023 |
Check for updates Robust Unsupervised Image to Template Registration Without Image Similarity Loss S Hachicha, C Le, V Wargnier-Dauchelle, M Sdika Medical Image Learning with Limited and Noisy Data: Second International …, 2023 | | 2023 |
Computer Analysis of Images and Patterns: CAIP 2019 International Workshops, ViMaBi and DL-UAV, Salerno, Italy, September 6, 2019, Proceedings M Vento, G Percannella, S Colantonio, D Giorgi, BJ Matuszewski, ... Springer Nature, 2019 | | 2019 |