Search Results for author:
Found 15 papers, 6 papers with code
Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET
Over the past years, pseudo-healthy reconstruction for unsupervised anomaly detection has gained in popularity.
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET
Unsupervised anomaly detection is a popular approach for the analysis of neuroimaging data as it allows to identify a wide variety of anomalies from unlabelled data.
A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation
We present a semi-supervised domain adaptation framework for brain vessel segmentation from different image modalities.
Frequency Disentangled Learning for Segmentation of Midbrain Structures from Quantitative Susceptibility Mapping Data
One may hypothesize that such property can be leveraged for better training of deep learning models.
Fourier Disentangled Multimodal Prior Knowledge Fusion for Red Nucleus Segmentation in Brain MRI
In this paper, we propose a new model that integrates prior knowledge from different contrasts for red nucleus segmentation.
Reproducibility in machine learning for medical imaging
Reproducibility is a cornerstone of science, as the replication of findings is the process through which they become knowledge.
Interpretability of Machine Learning Methods Applied to Neuroimaging
Here, we aim at providing answers to these questions by presenting the most common interpretability methods and metrics developed to assess their reliability, as well as their applications and benchmarks in the neuroimaging context.
Clinica: an open source software platform for reproducible clinical neuroscience studies
It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines.
Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder
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 autoencoder.
Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse
In order to train/validate the CNN, the data were annotated by two trained raters according to a visual QC protocol that we specifically designed for application in the setting of a data warehouse.
Visualization approach to assess the robustness of neural networks for medical image classification
We demonstrated that the areas identified by the CNN were consistent with what is known of Alzheimer's disease and that the visualization approach extract coherent longitudinal patterns.
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation
The different approaches generalized well to similar populations but not to datasets with different inclusion criteria or demographical characteristics.
Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimers disease
Lastly, with proper FR and FS, the performance of diffusion MRI features is comparable to that of T1w MRI.
Reproducible evaluation of classification methods in Alzheimer's disease: framework and application to MRI and PET data
We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data.
Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer's Disease
The core components are: 1) code to automatically convert the full ADNI database into BIDS format; 2) a modular architecture based on Nipype in order to easily plug-in different classification and feature extraction tools; 3) feature extraction pipelines for MRI and PET data; 4) baseline classification approaches for unimodal and multimodal features.
