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Found 34 papers, 8 papers with code
SMILE-UHURA Challenge -- Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms
The human brain receives nutrients and oxygen through an intricate network of blood vessels.
Confidence intervals uncovered: Are we ready for real-world medical imaging AI?
For more than 60% of papers, the mean performance of the second-ranked method was within the CI of the first-ranked method.
Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts
We performed automatic rating using a variety of deep learning models (conv5-FC3, ResNet and SECNN) as well as a ridge regression.
Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact Simulation
Subsequently, three artefact-specific models are pre-trained using these corrupted images to detect distinct types of artefacts.
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.
Classic machine learning methods
In this chapter, we present the main classic machine learning methods.
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.
How precise are performance estimates for typical medical image segmentation tasks?
An important issue in medical image processing is to be able to estimate not only the performances of algorithms but also the precision of the estimation of these performances.
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.
The impact of aging on human brain network target controllability
Understanding how few distributed areas can steer large-scale brain activity is a fundamental question that has practical implications, which range from inducing specific patterns of behavior to counteracting disease.
Axial multi-layer perceptron architecture for automatic segmentation of choroid plexus in multiple sclerosis
The conclusions of our paper are two-fold: 1) the studied deep learning methods could be useful tools to study CP in large cohorts of MS patients; 2)~Axial-MLP is a potentially viable alternative to convolutional neural networks for such tasks, although it could benefit from further improvements.
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.
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.
Step-wise target controllability identifies dysregulated pathways of macrophage networks in multiple sclerosis
In many applications it is often essential to test the ability of an individual node to control a specific target subset of the network.
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.
Gaussian Graphical Model exploration and selection in high dimension low sample size setting
We demonstrate on synthetic data that, when the sample size is small, the two methods produce graphs with either too few or too many edges when compared to the real one.
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.
Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training
Multiple sclerosis (MS) is a demyelinating disease of the central nervous system (CNS).
Learning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms
We propose a method to learn a distribution of shape trajectories from longitudinal data, i. e. the collection of individual objects repeatedly observed at multiple time-points.
Prediction of the progression of subcortical brain structures in Alzheimer's disease from baseline
We propose a method to predict the subject-specific longitudinal progression of brain structures extracted from baseline MRI, and evaluate its performance on Alzheimer's disease data.
Multilevel Modeling with Structured Penalties for Classification from Imaging Genetics data
Furthermore, we propose a framework allowing to combine several penalties taking into account the structure of the different types of data, such as a group lasso penalty over the genetic modality and a $L_2$-penalty on imaging modalities.
Statistical learning of spatiotemporal patterns from longitudinal manifold-valued networks
Model parameters show the variability of this average pattern of atrophy in terms of trajectories across brain regions, age at disease onset and pace of propagation.
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.
White Matter Fiber Segmentation Using Functional Varifolds
We use it to cluster fibers with a dictionary learning and sparse coding-based framework, and present a preliminary analysis using HCP data.
Multi-modal analysis of genetically-related subjects using SIFT descriptors in brain MRI
So far, fingerprinting studies have focused on identifying features from single-modality MRI data, which capture individual characteristics in terms of brain structure, function, or white matter microstructure.
Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging
We describe a new method to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features.
Robust imaging of hippocampal inner structure at 7T: in vivo acquisition protocol and methodological choices
Multi-slab registration yielded high quality datasets in 96 % of the subjects, thus compatible with further analyses of hippocampal inner structure. CONCLUSION:Multi-slab acquisition and registration setting is efficient for reducing acquisition time and consequently motion artifacts for ultra-high resolution imaging of the inner structure of the hippocampus.
Learning spatiotemporal trajectories from manifold-valued longitudinal data
The model allows to estimate a group-average trajectory in the space of measurements.
Spatial and anatomical regularization of SVM for brain image analysis
We show that Laplacian regularization provides a flexible framework to integrate various types of constraints and can be applied to both cortical surfaces and 3D brain images.
