Hi everyone! My name is Nathan Liu, and I am currently a second-year student at the University of Toronto, specializing in Statistics. From May to August 2025, I had the privilege of conducting an independent research project under the supervision of Dr. Pascal Tyrell. I am deeply grateful for his guidance throughout this journey. This was my first time having an independent research experience in data science, and it proved to be both challenging and rewarding. I would love to share some of the lessons I learned during this summer.
At the core of my project, I focused on the problem of automated grading of knee osteoarthritis (KOA) using deep learning. While recent work has shown promising results, the classification of Kellgren–Lawrence grade 2 (KL2) remains particularly unreliable. My study explored how self-supervised learning (SSL), specifically SimCLR embeddings, could be used to relabel ambiguous KL2 cases and improve classification performance. I designed four experimental pipelines: a baseline, a hard relabeling approach, a confidence-based relabeling approach, and a weighted loss strategy. Along the way, I incorporated quantitative evaluations such as bootstrap confidence intervals and McNemar’s test to assess improvements in KL2 reliability.
Before joining this project, I was already interested in the medical applications of machine learning, but I had never worked directly with this kind of research. I still remember my first lab meeting: Dr. Tyrell introduced a wide range of ongoing projects on different diseases, and I felt both excited and overwhelmed by the amount of new information. He warned us that the beginning would be the most difficult stage, but I underestimated just how challenging it would be. As I started exploring public databases, I quickly realized that many were incomplete, with missing labels and ambiguous annotations. This left me uncertain about how to begin. At this stage, I am thankful for the help I received from Noushin and Dr. Tyrell, as well as advice from a previous student in the lab. Their input helped me realize that I needed to commit to working with my own chosen dataset and design a study that I could take full ownership of.
During the research process, I encountered multiple challenges. The KL grading system itself is inherently noisy, and KL2 is especially difficult to identify consistently. On top of that, my dataset was imbalanced, which made model training unstable. Technically, training SimCLR models was not straightforward—convergence was slow, embeddings were difficult to interpret, and results were often not what I expected. Under Dr. Tyrell’s guidance, I learned to compare different baseline models, and switching from ResNet to EfficientNet immediately improved performance. He also encouraged me to experiment with visualization approaches beyond clustering, which eventually led me to explore spatial distance methods for relabeling KL2 cases. Noushin provided very practical advice on tuning SimCLR hyperparameters to maximize feature learning, which was critical to stabilizing my experiments. Throughout this process, I gained a new appreciation for how problem-solving in research often requires a mix of independent exploration, peer support, and careful reading of the literature.
Looking back, I am especially grateful for the structure of weekly lab meetings. They pushed me to stay disciplined, improve my efficiency, and keep refining my research plan. Just as importantly, they gave me the chance to see how other students tackled projects in different medical domains. I was struck by how many of us faced similar problems—unstable models, imperfect data, unexpected results—and it was reassuring to realize I was not alone. Watching others troubleshoot their difficulties often gave me ideas for my own work.
Overall, this project taught me valuable lessons both technically and personally. On the technical side, I became much more comfortable with self-supervised learning, parameter tuning, and methods for quantifying and visualizing results. On the personal side, I developed patience, resilience, and the ability to adapt when experiments did not go as planned. I also improved my academic writing skills and learned how to present my findings in a structured and convincing way. Most importantly, I am thankful to Dr. Tyrell for his constructive advice whenever I felt uncertain, and to Noushin for patiently answering many of my technical questions—even the simplest ones. I also want to thank my peers and all the lab members for their support, encouragement, and good company. This experience has not only strengthened my skills but has also made me more confident about pursuing research in medical imaging and machine learning in the future.