3.3k citations · 4.8k across the 43 of their papers we have counts for
6 papers · 1 filter
Privacy-Net: An Adversarial Approach for Identity-Obfuscated Segmentation of Medical Images
Bach Ngoc Kim, Jose Dolz, Pierre-Marc Jodoin +1
This paper presents a client/server privacy-preserving network in the context of multicentric medical image analysis. Our approach is based on adversarial learning which encodes im…
Cardiac MRI Segmentation with Strong Anatomical Guarantees
Nathan Painchaud, Youssef Skandarani, Thierry Judge +3
Recent publications have shown that the segmentation accuracy of modern-day convolutional neural networks (CNN) applied on cardiac MRI can reach the inter-expert variability, a gre…
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…
Spectral Metric for Dataset Complexity Assessment
Frederic Branchaud-Charron, Andrew Achkar, Pierre-Marc Jodoin
In this paper, we propose a new measure to gauge the complexity of image classification problems. Given an annotated image dataset, our method computes a complexity measure called…
Tractography and machine learning: Current state and open challenges
Philippe Poulin, Daniel Jörgens, Pierre-Marc Jodoin +1
Supervised machine learning (ML) algorithms have recently been proposed as an alternative to traditional tractography methods in order to address some of their weaknesses. They can…