Search Results for author:
Found 23 papers, 2 papers with code
DeepCERES: A Deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI
Finally, a new online pipeline, named DeepCERES, has been developed to make available the proposed method to the scientific community requiring as input only a single T1 MR image at standard resolution.
DeepThalamus: A novel deep learning method for automatic segmentation of brain thalamic nuclei from multimodal ultra-high resolution MRI
To make the proposed method fully available to the scientific community, a full pipeline able to work with monomodal standard resolution T1 images is also proposed.
3D Transformer based on deformable patch location for differential diagnosis between Alzheimer's disease and Frontotemporal dementia
In this paper, we present a novel 3D transformer-based architecture using a deformable patch location module to improve the differential diagnosis of Alzheimer's disease and Frontotemporal dementia.
Brain Structure Ages -- A new biomarker for multi-disease classification
Second, brain structure ages can be used to compute the deviation from the normal aging process of each brain structure.
Deep Grading based on Collective Artificial Intelligence for AD Diagnosis and Prognosis
In the first stage, we propose a deep grading model to extract meaningful features.
Deep grading for MRI-based differential diagnosis of Alzheimer's disease and Frontotemporal dementia
In this paper, we propose a deep learning based approach for both problems of disease detection and differential diagnosis.
Longitudinal detection of new MS lesions using Deep Learning
First, we propose to use transfer-learning from a model trained on a segmentation task using single time-points.
Interpretable differential diagnosis for Alzheimer's disease and Frontotemporal dementia
However, differential diagnosis of these two types of dementia remains difficult at the early stage of disease due to similar patterns of clinical symptoms.
Towards better Interpretable and Generalizable AD detection using Collective Artificial Intelligence
Current deep learning-based approaches in this field, however, have a number of drawbacks, including the interpretability of model decisions, a lack of generalizability information and a lower performance compared to traditional machine learning techniques.
Deep Anomaly Generation: An Image Translation Approach of Synthesizing Abnormal Banded Chromosome Images
We believe that this approach can be exploited for data augmentation of chromosome data sets with structural abnormalities.
POPCORN: Progressive Pseudo-labeling with Consistency Regularization and Neighboring
Semi-supervised learning (SSL) uses unlabeled data to compensate for the scarcity of annotated images and the lack of method generalization to unseen domains, two usual problems in medical segmentation tasks.
DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation
Instead of proposing another improvement of the segmentation accuracy, we propose a novel method robust to domain shift and performing well on unseen datasets, called DeepLesionBrain (DLB).
Deep correction of breathing-related artifacts in real-time MR-thermometry
Real-time MR-imaging has been clinically adapted for monitoring thermal therapies since it can provide on-the-fly temperature maps simultaneously with anatomical information.
RegQCNET: Deep Quality Control for Image-to-template Brain MRI Affine Registration
In the current study, a compact 3D convolutional neural network (CNN), referred to as RegQCNET, is introduced to quantitatively predict the amplitude of an affine registration mismatch between a registered image and a reference template.
Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort
Using clinical routine T1-weighted MRI, we evaluated the classification performance of: 1) univariate volumetry using two AVS (volBrain and Neuroreader$^{TM}$); 2) Support Vector Machine (SVM) automatic classifier, using either the AVS volumes (SVM-AVS), or whole gray matter (SVM-WGM); 3) reading by two neuroradiologists.
Tensor-Based Grading: A Novel Patch-Based Grading Approach for the Analysis of Deformation Fields in Huntington's Disease
In this work, we propose to combine the advantages of these two approaches by extending the patch-based grading framework with a new tensor-based grading method that enables us to model patterns of local deformation using a log-Euclidean metric.
AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation
Finally, we showed the interest of using semi-supervised learning to improve the performance of our method.
Multi-scale Graph-based Grading for Alzheimer's Disease Prediction
Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time.
AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation
Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images.
SuperPatchMatch: an Algorithm for Robust Correspondences using Superpixel Patches
Superpixels have become very popular in many computer vision applications.
An Optimized PatchMatch for Multi-scale and Multi-feature Label Fusion
On the EADC-ADNI dataset, we compare the hippocampal volumes obtained by manual and automatic segmentation.
Graph of brain structures grading for early detection of Alzheimer's disease
Alzheimer's disease is the most common dementia leading to an irreversible neurodegenerative process.
Non Local Spatial and Angular Matching : Enabling higher spatial resolution diffusion MRI datasets through adaptive denoising
We then introduce a spatially and angular adaptive denoising technique, the Non Local Spatial and Angular Matching (NLSAM) algorithm.
