Open Algorithms
DIY imaging-based algorithm
Expert of the month
The White Lab, McGill University
Ruwan Bedeir is a dedicated Master’s candidate with two years of experience in neuroscience research. She holds a B.Sc. Honours Specialization in Genetics from Western University and works at the White Lab. Ruwan’s primary research goal is to uncover the genetic factors behind white matter diseases, with a focus on developing targeted genetic therapies. She is actively involved in multiple projects that aim to differentiate between multiple sclerosis and similar conditions, investigate phenotypes and MR imaging in hereditary spastic paraplegia, and contribute to the White Matter Rounds Network. Her overarching mission is to make neuroscience more accessible to the public and advance our understanding of these complex conditions. Aley is a 4th year Economics student at Concordia University. His passion for computer programming and mathematics pushed him to pursue and obtain a Diploma in Data Science alongside his undergraduate studies. In 2023, he worked part-time as a statistics study group leader at the Student Success Center in Concordia University. Through academic and personal projects, he deepened his knowledge of machine learning algorithms and how to implement them to solve real world problems. As a volunteer for the lab, he uses his programming skills for custom program development as well as writing task automation scripts for data.
Seminar/Workshop
Wednesday, December 18, 2024 at 4 pm
de Grandpre Communications Centre, the Neuro (Montreal Neurological Institute-Hospital)
And on Zoom (register for link)
Our project is to develop an MRI-based algorithm to be used by radiologists and clinicians to ensure prompt recognition and interpretation of white matter abnormalities in patients with HSPs to accelerate the diagnostic process. In this session we will discuss how this project progressed from a flowchart, to a real functional tool, and teach you how to make your own. You will learn the workflow to compile your own database, what mistakes to look out for, and how to refine your search and use large databases (e.g. scopus). We will discuss how the algorithm was created and implemented, and give an overview of the tools we used to help plan and create the algorithm. We will briefly touch on topics such as Anaconda environments, python, and python packages, while explaining why we used these tools specifically, and what are alternatives suitable for your project.