24 citations · 30 across the 5 of their papers we have counts for
5 papers
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…
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…
Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort
Alexandre Morin, Jorge Samper-González, Anne Bertrand +12
BACKGROUND:Automated volumetry software (AVS) has recently become widely available to neuroradiologists. MRI volumetry with AVS may support the diagnosis of dementias by identifyin…
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…