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2020 – today
- 2024
[c7]Pauline Mouches, Thibaut Dejean, Julien Jung, Romain Bouet, Carole Lartizien, Romain Quentin:
Time CNN and Graph Convolution Network for Epileptic Spike Detection in Meg Data. ISBI 2024: 1-5- 2023
- [j4]Jasmine A. Moore
, Anup Tuladhar, Zahinoor Ismail, Pauline Mouches, Matthias Wilms, Nils D. Forkert:
Dementia in Convolutional Neural Networks: Using Deep Learning Models to Simulate Neurodegeneration of the Visual System. Neuroinformatics 21(1): 45-55 (2023) - [i2]Pauline Mouches, Thibaut Dejean, Julien Jung, Romain Bouet, Carole Lartizien, Romain Quentin:
Time CNN and Graph Convolution Network for Epileptic Spike Detection in MEG Data. CoRR abs/2310.09236 (2023) - 2022
- [j3]Raissa Souza, Pauline Mouches, Matthias Wilms, Anup Tuladhar, Sönke Langner, Nils D. Forkert:
An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction. J. Am. Medical Informatics Assoc. 30(1): 112-119 (2022) - [j2]Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert:
Invertible Modeling of Bidirectional Relationships in Neuroimaging With Normalizing Flows: Application to Brain Aging. IEEE Trans. Medical Imaging 41(9): 2331-2347 (2022) - [c6]Emma A. M. Stanley, Deepthi Rajashekar, Pauline Mouches, Matthias Wilms, Kira Plettl, Nils D. Forkert:
A fully convolutional neural network for explainable classification of attention deficit hyperactivity disorder. Medical Imaging: Computer-Aided Diagnosis 2022 - [c5]Matthias Wilms, Pauline Mouches, Jordan J. Bannister, Sönke Langner, Nils D. Forkert:
Disentangling Factors of Morphological Variation in an Invertible Brain Aging Model. MAD@MICCAI 2022: 95-107 - 2021
- [j1]Lazaro M. Sanchez-Rodriguez, Yasser Iturria-Medina, Pauline Mouches, Roberto C. Sotero:
Detecting brain network communities: Considering the role of information flow and its different temporal scales. NeuroImage 225: 117431 (2021) - [c4]Raissa Souza, Anup Tuladhar, Pauline Mouches, Matthias Wilms, Lakshay Tyagi, Nils D. Forkert:
Multi-institutional Travelling Model for Tumor Segmentation in MRI Datasets. BrainLes@MICCAI (2) 2021: 420-432 - [c3]Matthias Wilms, Pauline Mouches, Jordan J. Bannister, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert:
Towards Self-explainable Classifiers and Regressors in Neuroimaging with Normalizing Flows. MLCN@MICCAI 2021: 23-33 - [c2]Pauline Mouches, Matthias Wilms, Deepthi Rajashekar, Sönke Langner, Nils Daniel Forkert:
Unifying Brain Age Prediction and Age-Conditioned Template Generation with a Deterministic Autoencoder. MIDL 2021: 497-506 - 2020
- [c1]Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert:
Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flows. MLCN/RNO-AI@MICCAI 2020: 23-33 - [i1]Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert:
Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flows. CoRR abs/2011.13484 (2020)
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