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Found 13 papers, 3 papers with code
Towards frugal unsupervised detection of subtle abnormalities in medical imaging
This online approach is illustrated on the challenging detection of subtle abnormalities in MR brain scans for the follow-up of newly diagnosed Parkinsonian patients.
Anisotropic Hybrid Networks for liver tumor segmentation with uncertainty quantification
In this work we propose to compare two different pipelines based on anisotropic models to obtain the segmentation of the liver and tumors.
Multi-layer Aggregation as a key to feature-based OOD detection
Deep Learning models are easily disturbed by variations in the input images that were not observed during the training stage, resulting in unpredictable predictions.
TriadNet: Sampling-free predictive intervals for lesional volume in 3D brain MR images
The volume of a brain lesion (e. g. infarct or tumor) is a powerful indicator of patient prognosis and can be used to guide the therapeutic strategy.
Graph-based methods coupled with specific distributional distances for adversarial attack detection
We introduce a novel approach of detection and interpretation of adversarial attacks from a graph perspective.
Brain subtle anomaly detection based on auto-encoders latent space analysis : application to de novo parkinson patients
Neural network-based anomaly detection remains challenging in clinical applications with little or no supervised information and subtle anomalies such as hardly visible brain lesions.
Improving Uncertainty-based Out-of-Distribution Detection for Medical Image Segmentation
In this work, we evaluate various uncertainty frameworks to detect OOD inputs in the context of Multiple Sclerosis lesions segmentation.
Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature.
Nodal statistics-based equivalence relation for graph collections
Comparisons between generative models and real networks combining two different nodal statistics reveal the complexity of human brain functional connectivity with differences at both global and nodal levels.
Beyond Voxel Prediction Uncertainty: Identifying brain lesions you can trust
Deep neural networks have become the gold-standard approach for the automated segmentation of 3D medical images.
Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients
Although neural networks have proven very successful in a number of medical image analysis applications, their use remains difficult when targeting subtle tasks such as the identification of barely visible brain lesions, especially given the lack of annotated datasets.
ImUnity: a generalizable VAE-GAN solution for multicenter MR image harmonization
ImUnity is an original deep-learning model designed for efficient and flexible MR image harmonization.
Leveraging 3D Information in Unsupervised Brain MRI Segmentation
Learning is performed using a multicentric dataset of healthy brain MRIs, and segmentation performances are estimated on White-Matter Hyperintensities and tumors lesions.
