1 citations · 1 across the 4 of their papers we have counts for
5 papers
Structured Spectral Graph Representation Learning for Multi-label Abnormality Analysis from 3D CT Scans
Theo Di Piazza, Carole Lazarus, Olivier Nempont +1
With the growing volume of CT examinations, there is an increasing demand for automated tools such as organ segmentation, abnormality detection, and report generation to support ra…
Structured Spectral Graph Learning for Anomaly Classification in 3D Chest CT Scans
Theo Di Piazza, Carole Lazarus, Olivier Nempont +1
With the increasing number of CT scan examinations, there is a need for automated methods such as organ segmentation, anomaly detection and report generation to assist radiologists…
Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification
Theo Di Piazza, Carole Lazarus, Olivier Nempont +1
The rapid increase in the number of Computed Tomography (CT) scan examinations has created an urgent need for automated tools, such as organ segmentation, anomaly classification, a…
CT-3DFlow : Leveraging 3D Normalizing Flows for Unsupervised Detection of Pathological Pulmonary CT scans
Aissam Djahnine, Alexandre Popoff, Emilien Jupin-Delevaux +3
Unsupervised pathology detection can be implemented by training a model on healthy data only and measuring the deviation from the training set upon inference, for example with CNN-…
Comparing Deep Learning strategies for paired but unregistered multimodal segmentation of the liver in T1 and T2-weighted MRI
Vincent Couteaux, Mathilde Trintignac, Olivier Nempont +5
We address the problem of multimodal liver segmentation in paired but unregistered T1 and T2-weighted MR images. We compare several strategies described in the literature, with or…