5 citations · 14 across the 5 of their papers we have counts for
6 papers
Application of Homomorphic Encryption in Medical Imaging
Francis Dutil, Alexandre See, Lisa Di Jorio +1
In this technical report, we explore the use of homomorphic encryption (HE) in the context of training and predicting with deep learning (DL) models to deliver strict \textit{Priva…
Precision-Weighted Federated Learning
Jonatan Reyes, Lisa Di Jorio, Cecile Low-Kam +1
Federated Learning using the Federated Averaging algorithm has shown great advantages for large-scale applications that rely on collaborative learning, especially when the training…
Learn Faster and Forget Slower via Fast and Stable Task Adaptation
Farshid Varno, Lucas May Petry, Lisa Di Jorio +1
Training Deep Neural Networks (DNNs) is still highly time-consuming and compute-intensive. It has been shown that adapting a pretrained model may significantly accelerate this proc…
Dual Adversarial Inference for Text-to-Image Synthesis
Qicheng Lao, Mohammad Havaei, Ahmad Pesaranghader +3
Synthesizing images from a given text description involves engaging two types of information: the content, which includes information explicitly described in the text (e.g., color,…
InfoMask: Masked Variational Latent Representation to Localize Chest Disease
Saeid Asgari Taghanaki, Mohammad Havaei, Tess Berthier +4
The scarcity of richly annotated medical images is limiting supervised deep learning based solutions to medical image analysis tasks, such as localizing discriminatory radiomic dis…
Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation
Michal Drozdzal, Gabriel Chartrand, Eugene Vorontsov +6
In this paper, we introduce a simple, yet powerful pipeline for medical image segmentation that combines Fully Convolutional Networks (FCNs) with Fully Convolutional Residual Netwo…