2.2k citations · 3.1k across the 14 of their papers we have counts for
14 papers
Anomaly detection in image or latent space of patch-based auto-encoders for industrial image analysis
Nicolas Pinon, Robin Trombetta, Carole Lartizien
We study several methods for detecting anomalies in color images, constructed on patch-based auto-encoders. Wecompare the performance of three types of methods based, first, on the…
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…
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…
ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans
Audrey Duran, Gaspard Dussert, Olivier Rouvière +3
Multiparametric magnetic resonance imaging (mp-MRI) has shown excellent results in the detection of prostate cancer (PCa). However, characterizing prostate lesions aggressiveness i…
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,…
Learning to segment prostate cancer by aggressiveness from scribbles in bi-parametric MRI
Audrey Duran, Gaspard Dussert, Carole Lartizien
In this work, we propose a deep U-Net based model to tackle the challenging task of prostate cancer segmentation by aggressiveness in MRI based on weak scribble annotations. This m…
Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients
Verónica Muñoz-Ramírez, Nicolas Pinon, Florence Forbes +2
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 identif…
LU-Net: a multi-task network to improve the robustness of segmentation of left ventriclular structures by deep learning in 2D echocardiography
Sarah Leclerc, Erik Smistad, Andreas Østvik +9
Segmentation of cardiac structures is one of the fundamental steps to estimate volumetric indices of the heart. This step is still performed semi-automatically in clinical routine,…
Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography
Sarah Leclerc, Erik Smistad, João Pedrosa +11
Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has be…
Deep Learning Segmentation in 2D echocardiography using the CAMUS dataset : Automatic Assessment of the Anatomical Shape Validity
Sarah Leclerc, Erik Smistad, Andreas Østvik +9
We recently published a deep learning study on the potential of encoder-decoder networks for the segmentation of the 2D CAMUS ultrasound dataset. We propose in this abstract an ext…
Feature Selection for Unsupervised Domain Adaptation using Optimal Transport
Léo Gautheron, Ievgen Redko, Carole Lartizien
In this paper, we propose a new feature selection method for unsupervised domain adaptation based on the emerging optimal transportation theory. We build upon a recent theoretical…
Feature extraction with regularized siamese networks for outlier detection: application to lesion screening in medical imaging
Z. Alaverdyan, C. Lartizien
Computer aided diagnosis (CAD) systems are designed to assist clinicians in various tasks, including highlighting abnormal regions in a medical image. A common approach consists in…
Handling uncertainties in SVM classification
Emilie Niaf, Rémi Flamary, Carole Lartizien +1
This paper addresses the pattern classification problem arising when available target data include some uncertainty information. Target data considered here is either qualitative (…
GATE : a simulation toolkit for PET and SPECT
S. Jan, G. Santin, D. Strul +50
Monte Carlo simulation is an essential tool in emission tomography that can assist in the design of new medical imaging devices, the optimization of acquisition protocols, and the…