Pauline Mouches

According to our database, Pauline Mouches authored at least 11 papers between 2020 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Dementia in Convolutional Neural Networks: Using Deep Learning Models to Simulate Neurodegeneration of the Visual System.

Jasmine A. Moore, Anup Tuladhar, Zahinoor Ismail, Pauline Mouches, Matthias Wilms, Nils D. Forkert

Neuroinformatics, January, 2023

Time CNN and Graph Convolution Network for Epileptic Spike Detection in MEG Data.

Pauline Mouches, Thibaut Dejean, Julien Jung, Romain Bouet, Carole Lartizien, Romain Quentin

CoRR, 2023

2022

Invertible Modeling of Bidirectional Relationships in Neuroimaging With Normalizing Flows: Application to Brain Aging.

Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert

IEEE Trans. Medical Imaging, 2022

An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction.

Raissa Souza, Pauline Mouches, Matthias Wilms, Anup Tuladhar, Sönke Langner, Nils D. Forkert

J. Am. Medical Informatics Assoc., 2022

Disentangling Factors of Morphological Variation in an Invertible Brain Aging Model.

Matthias Wilms, Pauline Mouches, Jordan J. Bannister, Sönke Langner, Nils D. Forkert

Proceedings of the Medical Applications with Disentanglements - First MICCAI Workshop, 2022

A fully convolutional neural network for explainable classification of attention deficit hyperactivity disorder.

Emma A. M. Stanley, Deepthi Rajashekar, Pauline Mouches, Matthias Wilms, Kira Plettl, Nils D. Forkert

Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, 2022

2021

Detecting brain network communities: Considering the role of information flow and its different temporal scales.

Lazaro M. Sanchez-Rodriguez, Yasser Iturria-Medina, Pauline Mouches, Roberto C. Sotero

NeuroImage, 2021

Unifying Brain Age Prediction and Age-Conditioned Template Generation with a Deterministic Autoencoder.

Pauline Mouches, Matthias Wilms, Deepthi Rajashekar, Sönke Langner, Nils Daniel Forkert

Proceedings of the Medical Imaging with Deep Learning, 7-9 July 2021, Lübeck, Germany., 2021

Towards Self-explainable Classifiers and Regressors in Neuroimaging with Normalizing Flows.

Matthias Wilms, Pauline Mouches, Jordan J. Bannister, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert

Proceedings of the Machine Learning in Clinical Neuroimaging - 4th International Workshop, 2021

Multi-institutional Travelling Model for Tumor Segmentation in MRI Datasets.

Raissa Souza, Anup Tuladhar, Pauline Mouches, Matthias Wilms, Lakshay Tyagi, Nils D. Forkert

Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2021

2020

Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flows.

Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert

Proceedings of the Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology, 2020