821 citations · 944 across the 6 of their papers we have counts for
6 papers
Reproducibility in machine learning for medical imaging
Olivier Colliot, Elina Thibeau-Sutre, Ninon Burgos
Reproducibility is a cornerstone of science, as the replication of findings is the process through which they become knowledge. It is widely considered that many fields of science…
Interpretability of Machine Learning Methods Applied to Neuroimaging
Elina Thibeau-Sutre, Sasha Collin, Ninon Burgos +1
Deep learning methods have become very popular for the processing of natural images, and were then successfully adapted to the neuroimaging field. As these methods are non-transpar…
Clinica: an open source software platform for reproducible clinical neuroscience studies
Alexandre Routier, Ninon Burgos, Mauricio Díaz +21
We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to…
Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder
Clément Chadebec, Elina Thibeau-Sutre, Ninon Burgos +1
In this paper, we propose a new method to perform data augmentation in a reliable way in the High Dimensional Low Sample Size (HDLSS) setting using a geometry-based variational aut…
Visualization approach to assess the robustness of neural networks for medical image classification
Elina Thibeau Sutre, Olivier Colliot, Didier Dormont +1
The use of neural networks for diagnosis classification is becoming more and more prevalent in the medical imaging community. However, deep learning method outputs remain hard to e…
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation
Junhao Wen, Elina Thibeau-Sutre, Mauricio Diaz-Melo +7
Over 30 papers have proposed to use convolutional neural network (CNN) for AD classification from anatomical MRI. However, the classification performance is difficult to compare ac…