Hi everyone! My name is Abigail Chen, and I am a third-year student specializing in Data Science at the University of Toronto. This year, I had the opportunity to work on a research project in medical imaging through Professor Tyrrell’s lab. Looking back, the most difficult part of the experience was not coding or running experiments, but learning how to find a research question that actually mattered.
When I first started, I was very new to machine learning and honestly felt completely lost. Many of the terms, models, and technical ideas I encountered were unfamiliar to me, so even understanding the background well enough to follow the field took significant time and effort. Very early on, I realized that research is not just about trying a method and seeing what happens. Professor Tyrrell emphasized that a research question should always answer the question, “Who cares?” In other words, it is not enough for an idea to sound technical or interesting. It has to matter scientifically or clinically.
That point became very real when I first came up with a few possible project ideas and some of them were rejected. Professor Tyrrell pointed out that previous studies had already reported very high accuracy, leaving limited room for meaningful improvement. He then asked a question that completely changed the way I thought about the project: if these methods already perform so well, why are they still not commonly used in practice? That question pushed me to think beyond surface-level performance and focus instead on the gap between promising results and real-world usefulness. He also emphasized that if I wanted to study retinal disease, I needed to understand retinal disease itself well and become genuinely knowledgeable about the application area, not only the machine learning side.
This process eventually led me to the research question that shaped my final paper: whether uncertainty-aware modelling could improve the reliability of retinal OCT disease classification under domain shift. To investigate this, I compared a standard ResNet18 softmax classifier with an Evidential Neural Network on both internal and external OCT datasets collected from different scanners and institutions. The study showed that although the baseline model achieved stronger internal accuracy, the uncertainty-aware model was substantially more stable across external datasets, with a much smaller internal-to-external performance drop. It also produced useful uncertainty estimates that supported more cautious decision-making, and after additional refinement with threshold tuning and selective prediction, the model achieved stronger retained-case external performance with lower false-negative and false-positive rates. These findings made the project feel especially meaningful to me, because they connected technical modelling choices to a practical clinical question: not only whether a model can make a prediction, but whether that prediction should actually be trusted.
Overall, this experience taught me that research begins long before the final experiment. It begins with learning the background, understanding the real problem, and asking a question that is specific, meaningful, and worth answering. I am very grateful to Professor Tyrrell for pushing me to think more critically about what makes research important, and this experience has given me a much deeper appreciation of how serious, demanding, and rewarding research can be.