Lessons Along the Way

With summer almost here, it’s a good time to reflect on lessons learned from the academic year gone by. Since September, I’ve been working under Dr. Pascal Tyrrell’s supervision on a systematic review (SR) project investigating sample size determination methods (SSDMs) in machine learning (ML) applied to medical imaging. Shout out to the Department of Statistical Sciences where I completed my independent studies course! Here, I share important lessons I learned in the hopes that they may resonate with you.
Despite being a stats student (as you know from my previous posts!), I was initially new to ML and confronted with the task of critically reviewing theoretically-dense primary articles. I came to appreciate the first step was to develop a solid background – starting from high-level YouTube videos and lessons on DataCamp, to reading ML blogs and
review articles – all until I was confident enough to evaluate articles on my own. For me, the key to learning a complex subject was to build on foundational concepts and keep things as clear as possible. As Einstein once said: “If you can’t explain it simply, you don’t understand it well enough”.
Next, it was time to conduct a systematic search. The University of Toronto library staff were especially helpful at guiding me in use of OVID Medline and Embase, databases with methodical search procedures and a careful search syntax relying on various operators. To be thorough, we also sent a request out to the rest of our research team, who hand-searched through their own stash of literature. Along the way, we garnered support from the university, successfully receiving the Undergraduate Research
Fund grant. The lessons for me here? The importance of seeking expert help where appropriate, and that being resourceful can pay off (literally)! Finally, I valued our strong team culture, without which none of this would have been possible.
While working on the SR, I also conducted a subsampling experiment using a medical imaging dataset, testing the effect of class imbalance on a classifier’s performance. Hands-on/practical experiences are critical in developing a more nuanced understanding of subject material – in my case, an understanding that translated to my SR.
So now you are probably wondering about the results! The subsampling experiment helped us develop a model for the deleterious effect of class imbalance on classification accuracy and demonstrated that this effect was sensitive to total sample size. Meanwhile in our SR, we observed great variability in SSDMs and model assessment measures, calling for the need to standardize reporting practices.
That was a whirlwind recap of the year and I hope some of the lessons I learned resonate with you!
See you in the
Indranil Balki
A special thanks to Dr. Pascal Tyrrell, as well as Dr.
Afsaneh Amirabadi & Team