9 citations · 19 across the 8 of their papers we have counts for
9 papers
PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies
Mehdi Ounissi, Morwena Latouche, Daniel Racoceanu
Quantifying the phagocytosis of dynamic, unstained cells is essential for evaluating neurodegenerative diseases. However, measuring rapid cell interactions and distinguishing cells…
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…
Visual deep learning-based explanation for neuritic plaques segmentation in Alzheimer's Disease using weakly annotated whole slide histopathological images
Gabriel Jimenez, Anuradha Kar, Mehdi Ounissi +5
Quantifying the distribution and morphology of tau protein structures in brain tissues is key to diagnosing Alzheimer's Disease (AD) and its subtypes. Recently, deep learning (DL)…
Computational Pathology for Brain Disorders
Gabriel Jimenez, Daniel Racoceanu
Non-invasive brain imaging techniques allow understanding the behavior and macro changes in the brain to determine the progress of a disease. However, computational pathology provi…
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…
eXclusive Autoencoder (XAE) for Nucleus Detection and Classification on Hematoxylin and Eosin (H&E) Stained Histopathological Images
Chao-Hui Huang, Daniel Racoceanu
In this paper, we introduced a novel feature extraction approach, named exclusive autoencoder (XAE), which is a supervised version of autoencoder (AE), able to largely improve the…