Squeezing in a Little Time for ML this Past Summer: John Valen’s Experience

My name is John Valen. Having recently completed my undergraduate degree in statistics and economics here at U of T, and soon moving on to pursue my Master’s in statistics in Europe, the Medical Imaging Volunteer Internship program seemed almost tailored to my goal of getting valuable research experience within a constrained time window. Over the course of only several months this summer, I’ve had the pleasant and enriching experience of contributing ideas and code to the project that summer ROP student Wenda Zhao undertook for the dentistry department at U of T, along with the guidance and contributions of ML lab leader Hershel Stark.

Wenda’s blog post (see here) neatly summarizes the goal of this project, one whose aim is to determine the likelihood that a misdiagnosis may occur, depending on the degree of damage to the dental plate being used for X-rays. Contributions I’ve helped make in particular include:

– Creating sparse matrix representations of the grey scale X-ray images themselves in order to economize on memory and run-time performance
– Hand-engineering features: once the artifacts (damage such as scratches, dents,
blotches, etc) were segmented out via DBSCAN, they were characterized by a variety of different metrics: size (pixel count), average pixel intensity (images are grey scale), location (relative to the center of the plate image), etc. 

– Training a K-Means algorithm to cluster segmented artifacts from the dental plate images based on these hand-engineered features, whereby clustering them in this unsupervised manner gave us insight on their properties;

And much more. If you are not familiar with this machine learning lingo, then do not worry; I was hardly exposed to it myself before I started working in this lab. I went in knowing close to nothing practical and a whole lot theoretical, and came out knowing quite a little more in the way of the first one. Fine, a lot more: or
so I like to think. It may not seem clear how my contributions can be used in the future to help answer the ultimate question. The truth is, nothing is really clear at the moment. The project is still on-going and I intend to keep up with it, making contributions remotely to it while I am away in Belgium pursuing my Master’s degree. This is the greatness of it all, the amount of flexibility we have in answering these questions leaves a lot of room for creativity and contemplation. 

All in all, from my own perspective (which has been greatly expanded over the course of the summer), the volunteer program was a perfect means to experience the sheer amount of work that is enthusiastically undertaken by serious students in answering these important questions. I hope that I too can now consider myself at the very least climbing to their ranks while I move on to other and more numerous serious pursuits in my life. 

Good luck to you all, and do not underestimate yourselves.

John Valen

Lee Radigan: A Reflection on my (6th) Year as an Undergrad at the University of Toronto

My name is Lee Radigan and I am a non-degree student pursuing admittance to the Biostatistics Masters program at the Dalla Lana School of Public Health.  After returning for my 6th year studying statistics at The University of Toronto, I thought that this was a perfect time to reflect on my progress.
Since September, I have been working under Dr. Pascal Tyrrells guidance on a project aimed at helping the Department of Medical Imaging report agreement in their research.  To do this, I created a flow chart to help guide the reader towards the proper method of agreement.  Along with this, I conducted a simulation looking at a specific question pertaining to the Department.
Initially, I was tasked with combing through various papers on the theory of agreement and making sense of all the different published work that was out there.  There are many different approaches and different ways of looking at reporting agreement, so it was quite difficult to figure out when and where to properly use every single approach.  After reading and re-reading each paper, as well as consulting the MiData team, I started to develop a thorough understanding of what agreement was, why it is important to report it, and how to go about reporting it appropriately.
Next, a flow chart was required to summarize what I had learned from the literature.  This was not an easy task, because it forced me to dig really deep and make sure that every node in my flow chart was well thought out and appropriate.  After many iterations and adjustments, I created a detailed chart that walks the reader from their initial research question up to the required agreement statistic.
My final task was to conduct a simulation that would test the question: Can a group of less experienced student raters be as accurate as a smaller set of more experience expert raters?  And if so, how many students?  And under what conditions?  This was a very fun and informative task for me as I was able to conduct my first simulation.  During this experience, my biggest difficulty was justifying my choices of parameters within the simulation.  When conducting a simulation you have freedom to choose how it is going to work, but you must be careful to be able to back up each and every parameter choice.  The simulation ended up showing that: the larger the disparity between the rating errors of the student and expert raters, the more students it takes to match the accuracy of the experts, confirming my intuition.
There are many things that I wish to expand on with respect to my project in future.  I want to create a user friendly app that will be even easier and more compact than my flow chart.  Additionally, I want to try to get my paper published.  To do this I will need to look further into my simulation and consider a more broad range of student/expert scenarios that likely will occur in practice.  I will also need to further refine my definitions and understanding of each concept of agreement.
This year has truly been the best of my life and I can largely attribute that to Dr. Pascal and the MiData team.  I look forward to contributing to Medical Imaging research and to many more learning experiences.
Time to enjoy the summer as I embark on yet another exciting experience as a student Statistical Analyst at the CAMH Nicotine Dependence Clinic as a summer placement!
Lee Radigan

MiVIP meets AI…

Well, I think it was inevitable. My data science lab has slowly crossed over to the dark side into the world of  Machine Learning and Artificial Intelligence.

Let me apologize for being MIA for so long. Life has been pretty hectic these past months as I have been building the MiDATA program here in the Department of Medical Imaging at the University of Toronto. The good news is that the MiVIP program will now be inviting students to participate in machine learning and artificial intelligence in medical image research.

This summer will include the launch our our MiStats+ML program where we will have students from the department of statistical sciences, computer sciences, and life sciences all work together on ML/AI projects in the MiDATA lab.

Stay tuned as we ramp up and get back to some our previous threads like MiWORD of the day…

See you in the blogosphere,


Wow! What a Busy Summer….

Jenny Joo – YSP 2016

Over 20 students in the lab this summer beavering away at some great projects. Last week my two Youth Summer Program (University of Toronto) students finished their three week stay with us. 

Jenny and Michelle both did fantastic work.

Today Jenny will show you her poster entitled:“Comparing Healthy and Unhealthy Carotid Arteries”

Jenny Joo is from Richmond Hill, Ontario, entering her senior year of high school. She plans on studying life science at the University
of Toronto in the future. She spent the last 3 weeks in U of T’s YSP Medical
Research program, where she was placed in two different medical imaging labs: The
MiDATA lab of U of T and the Vascular Biology Imaging Research lab at
Sunnybrook Hospital. 

Jenny chose to do research on the
MRI scans of the carotid artery because it focused on both research and
clinical aspects and had this to say about her experience with us: “It has been an enriching 3 weeks working with my PI, Pascal
Tyrrell, my mentors, John Harvey and Moran Foster, and the rest of the research

Great work Jenny Joo!

Have a peek at her poster and…

… I’ll see you in the blogosphere.

Pascal Tyrrell

MiDATA – Enabling Medical Image Research at the University of Toronto

Poster to be presented at the Department of Medical Imaging Resident Achievement Day 2016

Where have a I been you ask? At my desk putting this program together! I apologize for being MIA for the past month or so but I it has been a busy time nurturing this fledgling program of MiNE (pun intended!).

Here is the premise:

Bridging the gap between clinical expertise and the science of managing and analyzing medical imaging data is challenging. To provide direction for data management as well as the analysis and reporting of research findings, we are in the process of introducing a data science unit – MiDATA – offering users an environment geared towards a “soup to nuts” approach to medical imaging research methodology and statistics. The Department of Medical Imaging of the University of Toronto is one of the largest in North America with a clinical faculty of more than 184 faculty, 60 residents and 80 fellows based at nationally and internationally renowned hospitals conducting cutting edge clinical research in the greater Toronto area. The challenge of any successful research and educational program is bridging the “know-do” gap. The goal of MiDATA is to facilitate impactful research through the efficient and creative use of a mentored learning environment.

Shout out to our collaborators the Division of Biostatistics from the Dalla Lana School of Public Health!

Tomorrow is the official unveiling at the 2016 Department of Medical Imaging Resident Achievement Day. I thought I would share with you our poster as a sneak peek…

Once you have digested its contents have a listen to Paper Planes by M.I.A. to decompress and…

… I’ll see you in the blogosphere (or at tomorrow’s event!)

Pascal Tyrrell