This project aims to create data pipelines in diagnostic radiology in order to supply analysis and visualization tools.

The first pipeline is intended for data anonymization according to standard DICOM while the second one allows to supply Kibana (Elasticsearch) or Superset (Apache) platforms.

The Airflow orchestrator (Apache) is used to automate the execution of pipelines which could eventually supply dynamic dashboards. 
 

The research project is about the suitability of laws, legal principles and general framework surrounding health-related data, including those regulating the involved medical liability, in Canada and in the European Union. 
It aims to identify its weaknesses and aspires to provide regulatory solutions that are more appropriate to the realities of artificial intelligence. These solutions should better balance private and public, individual, social, commercial and health-related interests at stake. Also, this project considers a different view of the law and of our current legal systems with missing satisfactory answers.
 

This project focuses on the use of machine learning techniques in external radiotherapy for cancer treatment planning.
Stochastic frontier analysis is a parametric approach used in econometrics and appropriated for medical physics. Using a retrospective bank of treated patients it will be possible to predict the optimal dose of radiation for tumor and healthy organs.
This method is applied to multiple cancer treatment sites which emerge new challenge in the context of prediction, and data processing.

The characterization of Drug-Drug interactions (DDIs) is crucial for planning therapies and drugs
co-administration. While considerable efforts are spent in labor-intensive in vivo experiments and time-consuming clinical trials, understanding the pharmacological implications and adverse side-effects for some drug combinations is challenging. The joint impact of the majority of combinations remains undetected until therapies are prescribed to patients. This raises the need for computational tools predicting DDIs in order to reduce experimental costs and exhaustively characterize all drug combination effects before therapy recommendations. 
Previous attempts to build such tools focused on pharmacodynamic and pharmacokinetic interactions and used features that are difficult to access in the early stages of R&D. 
In this work, we propose to use data about the drugs and their targets (pathways, biomarkers, gene expressions, etc) that are available at the beginning of each drug R&D campaign. Our hypothesis is that high-level deep learning features extracted from those data will improve DDI characterization. Therefore, our models will be trained to output the pharmacological effects of DDIs as well as underlying molecular and biological pathway interactions. 
Creating such a comprehensive toolkit will help to reduce risks in polypharmacy therapies.
 

The research project is focused on the synthesis of medical images using deep learning, towards better artifact correction and the avoidance of unnecessary medical procedures.

The neural networks designed in this project have a flexible architecture enabling the image synthesis from only an heterogeneous subset of input modalities. The images are synthesized in pathological situations, such as Alzheimer's disease and brain cancers.

This project is centered on an examination of the process of preparing a patient-centered media and social media strategy that provides patients with useful information about the Opal patient portal and how they can make the most of it.