3 citations · 8 across the 6 of their papers we have counts for
6 papers
Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact Simulation
Sophie Loizillon, Simona Bottani, Stéphane Mabille +6
The emergence of clinical data warehouses (CDWs), which contain the medical data of millions of patients, has paved the way for vast data sharing for research. The quality of MRIs…
Frequency Disentangled Learning for Segmentation of Midbrain Structures from Quantitative Susceptibility Mapping Data
Guanghui Fu, Gabriel Jimenez, Sophie Loizillon +8
One often lacks sufficient annotated samples for training deep segmentation models. This is in particular the case for less common imaging modalities such as Quantitative Susceptib…
Fourier Disentangled Multimodal Prior Knowledge Fusion for Red Nucleus Segmentation in Brain MRI
Guanghui Fu, Gabriel Jimenez, Sophie Loizillon +9
Early and accurate diagnosis of parkinsonian syndromes is critical to provide appropriate care to patients and for inclusion in therapeutic trials. The red nucleus is a structure o…
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…
Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse
Simona Bottani, Ninon Burgos, Aurélien Maire +4
Many studies on machine learning (ML) for computer-aided diagnosis have so far been mostly restricted to high-quality research data. Clinical data warehouses, gathering routine exa…