most citedOne-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities

2 citations · 3 across the 4 of their papers we have counts for

collaborators

7 papers

eess.IV20231 cited

Whole-brain radiomics for clustered federated personalization in brain tumor segmentation

Matthis Manthe, Stefan Duffner, Carole Lartizien

Federated learning and its application to medical image segmentation have recently become a popular research topic. This training paradigm suffers from statistical heterogeneity be…

cs.CV20232 cited

Time CNN and Graph Convolution Network for Epileptic Spike Detection in MEG Data

Pauline Mouches, Thibaut Dejean, Julien Jung +3

Magnetoencephalography (MEG) recordings of patients with epilepsy exhibit spikes, a typical biomarker of the pathology. Detecting those spikes allows accurate localization of brain…

eess.IV2023

Towards frugal unsupervised detection of subtle abnormalities in medical imaging

Geoffroy Oudoumanessah, Carole Lartizien, Michel Dojat +1

Anomaly detection in medical imaging is a challenging task in contexts where abnormalities are not annotated. This problem can be addressed through unsupervised anomaly detection (…

eess.IV20232 cited

One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities

Nicolas Pinon, Robin Trombetta, Carole Lartizien

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. Patch-based repr…

eess.IV2023

Brain subtle anomaly detection based on auto-encoders latent space analysis : application to de novo parkinson patients

Nicolas Pinon, Geoffroy Oudoumanessah, Robin Trombetta +3

Neural network-based anomaly detection remains challenging in clinical applications with little or no supervised information and subtle anomalies such as hardly visible brain lesio…

eess.IV2022

Perfusion imaging in deep prostate cancer detection from mp-MRI: can we take advantage of it?

Audrey Duran, Gaspard Dussert, Carole Lartizien

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,…