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Found 14 papers, 2 papers with code
Margin-aware Adversarial Domain Adaptation with Optimal Transport
In this paper, we propose a new theoretical analysis of unsupervised domain adaptation that relates notions of large margin separation, adversarial learning and optimal transport.
Whole-brain radiomics for clustered federated personalization in brain tumor segmentation
We propose a novel personalization algorithm tailored to the feature shift induced by the usage of different scanners and acquisition parameters by different institutions.
Time CNN and Graph Convolution Network for Epileptic Spike Detection in MEG Data
Magnetoencephalography (MEG) recordings of patients with epilepsy exhibit spikes, a typical biomarker of the pathology.
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.
Anomaly detection in image or latent space of patch-based auto-encoders for industrial image analysis
We study several methods for detecting anomalies in color images, constructed on patch-based auto-encoders.
One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities
Anomaly detection remains a challenging task in neuroimaging when little to no supervision is available and when lesions can be very small or with subtle contrast.
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.
ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans
In this work, we propose a novel end-to-end multi-class network that jointly segments the prostate gland and cancer lesions with GS group grading.
Perfusion imaging in deep prostate cancer detection from mp-MRI: can we take advantage of it?
To our knowledge, all deep computer-aided detection and diagnosis (CAD) systems for prostate cancer (PCa) detection consider bi-parametric magnetic resonance imaging (bp-MRI) only, including T2w and ADC sequences while excluding the 4D perfusion sequence, which is however part of standard clinical protocols for this diagnostic task.
Learning to segment prostate cancer by aggressiveness from scribbles in bi-parametric MRI
Performance is assessed on a private dataset (219 patients) where the full ground truth is available as well as on the ProstateX-2 challenge database, where only biopsy results at different localisations serve as reference.
LU-Net: a multi-task network to improve the robustness of segmentation of left ventriclular structures by deep learning in 2D echocardiography
Results obtained on a large open access dataset show that our method outperforms the current best performing deep learning solution and achieved an overall segmentation accuracy lower than the intra-observer variability for the epicardial border (i. e. on average a mean absolute error of 1. 5mm and a Hausdorff distance of 5. 1mm) with 11% of outliers.
Prostate Cancer Semantic Segmentation by Gleason Score Group in bi-parametric MRI with Self Attention Model on the Peripheral Zone
Performance of our model was compared to a U-Net baseline model to assess the impact of the self attention module on PCa detection.
Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography
Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis.
Feature Selection for Unsupervised Domain Adaptation using Optimal Transport
In this paper, we propose a new feature selection method for unsupervised domain adaptation based on the emerging optimal transportation theory.
