28 citations · 28 across the 4 of their papers we have counts for
6 papers
Effect of Prior-based Losses on Segmentation Performance: A Benchmark
Rosana El Jurdi, Caroline Petitjean, Veronika Cheplygina +2
Today, deep convolutional neural networks (CNNs) have demonstrated state-of-the-art performance for medical image segmentation, on various imaging modalities and tasks. Despite ear…
Breaking the Limits of Message Passing Graph Neural Networks
Muhammet Balcilar, Pierre Héroux, Benoit Gaüzère +3
Since the Message Passing (Graph) Neural Networks (MPNNs) have a linear complexity with respect to the number of nodes when applied to sparse graphs, they have been widely implemen…
High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey
Rosana El Jurdi, Caroline Petitjean, Paul Honeine +2
Today, deep convolutional neural networks (CNNs) have demonstrated state of the art performance for supervised medical image segmentation, across various imaging modalities and tas…
Statistical learning for sensor localization in wireless networks
Daniel Alshamaa, Farah Chehade, Paul Honeine
Indoor localization has become an important issue for wireless sensor networks. This paper presents a zoning-based localization technique that uses WiFi signals and works efficient…
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks
Muhammet Balcilar, Guillaume Renton, Pierre Heroux +3
This paper aims at revisiting Graph Convolutional Neural Networks by bridging the gap between spectral and spatial design of graph convolutions. We theoretically demonstrate some e…
Bayesian Filtering of Smooth Signals: Application to Altimetry
Abderrahim Halimi, Gerald S. Buller, Steve McLaughlin +1
This paper presents a novel Bayesian strategy for the estimation of smooth signals corrupted by Gaussian noise. The method assumes a smooth evolution of a succession of continuous…