Ongoing projects

Directeur.e(s) de recherche
Anne-Sophie Charest
Start date
Title of the research project
Statistical Analysis of Synthetic Data Sets Satisfying Differential Confidentiality
Description
Description

Data sharing is often limited by privacy issues. This is very common in particular for health datasets, given the inherent sensitivity of this type of data. When sharing of the original dataset is not possible, one method that can be used is to generate a synthetic dataset, which contains as much statistical information as possible from the original dataset, but which provides data on false individuals in order to protect the confidentiality of respondents.

This project is interested in rigorously measuring the confidentiality protection offered by a synthetic dataset. We will carefully examine some measures proposed in the literature, to understand their guarantees and the differences and similarities between them in order to identify the measure (s) that would be the most relevant for the sharing of synthetic data.

Student
Directeur.e(s) de recherche
Catherine Régis
Anne Debet
Start date
Title of the research project
The health data legal framework and associated medical liability mechanisms, within the artificial intelligence development framework : comparative European and North American prospects
Description
Description

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.
 

Student
Directeur.e(s) de recherche
John Kildea
Start date
Title of the research project
Determination of the mutational signature of ionizing radiation using single-cell sequencing
Description
Description

This research project aims to examine the mutational signature of ionizing radiation using single-cell sequencing techniques.

The project is using human lymphoblastoid cells donated by the Ashkenazi trio that have a well characterized genome. The cells are irradiated and sequenced to determine the mutations that are induced as a result of the exposure to the ionizing radiation.

Through biostatistical analysis of the human genomic data thus obtained, we will be able to identify the mutational signature of ionizing radiation.
 

Student
Directeur.e(s) de recherche
Philippe Després
Start date
Title of the research project
Personalized dosimetry in computed tomography imaging
Description
Description

The aim of this doctoral thesis is to develop a tool able to automatically provide organs of interest segmentation in computed tomography images using machine learning techniques.

This tool will then be used to calculate organ doses in order to establish personalized dosimetric records in medical imaging. Doses will be calculated using informations obtained from images, radiographic technique and GPU-based Monte Carlo dose calculation algorithm (GPUMCD). Automated pipelines will be implemented to process large amounts of data.

The proposed tool provides a better evaluation of population exposure to ionizing radiation caused by medical imaging procedures.

Student
Directeur.e(s) de recherche
Philippe Després
Start date
Title of the research project
Automated extraction pipelines in medical imaging
Description
Description

The objective of this project is to extract a set of relevant data from the files produced by medical imaging devices.

The process consists of building ETL (extract-transform-load) pipelines to make the data consumable for analysis and visualization. An example of analysis consists in observing the trend in dose administered to patients according to the establishment, protocol or device used, in order to possibly identify non-standard practices.

The data extracted could also guide practice by making it possible to assess the relevance of certain studies, and thus to optimize resources in the health network.

Directeur.e(s) de recherche
Louis Archambault
Start date
Title of the research project
Role of machine learning in prostate cancer magnetic resonance imaging radiotherapy target definition
Description
Description

The clinical and economic burden of prostate cancer in Canada is substantial and is rising. It has been indicated that 1 in 7 men will develop prostate cancer during their lifetime, and another 1 in 27 will die due to the prostate cancer. However, only a part of prostate cancer cases is clinically important which make the prostate cancer case discrimination crucial to avoid over-treatment. Compared to ultrasound imaging method, advanced MRI modalities have demonstrated a better diagnostic accuracy and is becoming a clinical routine examination for patients at risk of having clinically significant prostate cancer. Even though the version two of PI-RADS has been recently published to facilitate MRI modalities application in prostate cancer, they still present limitations. For instance, variability is reported in terms of inter-reader agreement and diagnosis accuracy, mainly depend on reader experience.

This project aims to find a machine learning based approach for predication and segmentation of intraprostatic lesions to better guide radiation treatment. For accomplishing this task, the most advanced MRI modalities including DTI-MRI and DWI-MRI along with the anatomical MRI modalities will be employed. From the quantitative MRI modalities several maps that enhance specific features of the lesion will be extracted. Then after, texture information of the MRI modalities and selected maps will be extracted. In this step machine learning methods will be employed for feature selection and classification purposes. Finally, the prostate cancer extension and its type are identified.

Student
Directeur.e(s) de recherche
Jacques Corbeil
Start date
Title of the research project
Multi-view supervised machine learning for solving multi-omics problems
Description
Description

Supervised classification allows to build predictive models based on complex data to help human decision making processes. It has undergone an impressive development in recent years, particularly thanks to neural networks and the use of big data. However, these methods are not relevant to use on databases in which only a few instances are available to build the model, and even less when these instances are described by a large number of features. This type of problem, called fat data, is recurrent in the medical field, in which the extraction of data on patients is costly, but provides a large amount of information for each one. Moreover, in the medical field, it is common to perfrom several types of analysis on the same patient : genomic, metabolomic, transcriptomic, etc. This type of database is called multi-omics.

The goal of this project is to use and develop multi-view classification algorithms relevant to the processing of multi-omic fat data

Directeur.e(s) de recherche
Louis Archambault
Frédéric Pouliot
Start date
Title of the research project
Radical prostatectomy preoperative radiomic analysis to predict lymph node metastasis in high-grade prostate cancers
Description
Description

Prostate cancer is the most common form of cancer in men in Canada.

This research project aims to establish a prognosis for a patient suffering from prostate cancer as well as predict the final pathology, by predicting the presence of lymph node metastases, from a FDG PET-CT. Radiomic characteristics are defined as the process of quantitative extraction of usable high-dimensional data from medical images. These are biomarkers that are difficult to see with the naked eye, such as texture and intensity. The database is made up of 250 prostate cancer patients. After filtration, a subset of 331 radiomic characteristics was selected. The accuracy of the model is 74.5%. This corresponds to an increase in precision of 6% compared to a model trained on all the extracted characteristics.

Ultimately, the algorithm will better predict the risk of recurrent prostate cancer and help improve methods and choice of treatment.

Student
Directeur.e(s) de recherche
John Kildea
Start date
Title of the research project
Use of natural language processing, radiomics and patient-reported outcomes to improve radiotherapy in cancer patients with bone metastases
Description
Description

The primary objective of this research project is to detect cancer pain at an early stage by analyzing patients’ medical images. 
Development of an algorithm to do this can be achieved by combining two computer science techniques: one that allows us to gather information about pain from medical notes, and one that extracts information from medical images. We will use the first technique in a computer program that will extract and quantify pain intensity recorded in patients' medical notes. 
The second technique will be employed in another program that will analyze radiographic images of cancer patients’ to extract information about their bone metastases (such as tumor volume, and shape). Then, we will implement advanced statistical and mathematical techniques to model the relationship between identified tumor features and extracted pain intensities. 
Finally, to validate our model, we will use pain scores that are directly collected from thousands of future cancer patients via a mobile app that has been developed in our group (opalmedapps.com).
 

Student
Directeur.e(s) de recherche
John Kildea
Start date
Title of the research project
Use of patient-reported outcomes and accumulated dose for accurate quantification of the dose-outcome relationship in hypofractionated prostate cancer radiotherapy
Description
Description

This project is focused on determining if patient-reported outcomes are better correlated with actual dose delivered than with planned dose in prostate cancer patients receiving radiotherapy.

The project will use daily cone-beam CT images to calculate the daily and total radiation dose delivered to patients, and the Opal app to collect their patient-reported outcomes. 

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Featured project

Radiotherapy treatments currently used in the clinical field are rarely modified. They generally consist of a global therapy of 50 grays, fractionated in five treatments of two grays every week for five weeks.
Thus, it could be worthwhile to develop a numeric tool, based on mathematical models found in the literature, in order to compare different types of treatment without having to test them on real tissues. Several parameters are known to alter the tissue response after irradiation including oxygen

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