Hey folks! My name is Will Wu. I have just finished my second year at the University of
Toronto, currently pursuing a Statistics Specialist and Computer Science minor. Recently, I have just wrapped up my final paper on the ROP project with Professor Pascal Tyrrell. Looking back on the entire experience of doing this ROP, I feel grateful that I could have such an opportunity to learn and engage in research activities, so I find it meaningful to share my experience in the lab!
In the first couple of meetings that I attend, I sometimes find it difficult to follow up and
understand the concepts or projects that they discuss or introduce during the lab meeting, but Professor Tyrrell would usually explain these concepts that we are unfamiliar with. As I work more on the slide deck about Machine Learning, I begin to be familiar with some of the common AI knowledge, the logic behind the neural network and most importantly its significance in medical imaging.
When I am looking for an area of research that is related to Machine Learning as well as
medical imaging, Professor Tyrrell introduced us to a few interesting topics, and one of them is about domain shift. After a bit of literature review on this topic, I further grasp some knowledge about catastrophic forgetting, domain adaptation and out-of-distribution shift. Domain shift represents a shift in the data distribution when a deep learning model sees an unseen new set of data from a different dataset. This often occurs in the medical imaging area as images from different imaging centers have different acquisition tools or rules, which might lead to a difference between datasets. Therefore, I found it interesting to see the impact domain shift would bring on the performance of a CNN model, and how to quantify such a shift, especially on regular CT scans and low-dose CT scans.
For my project, it would require training and retraining the CNN model to observe such
an impact on the model performance, and it often leads to frustration for me as errors and
potential risks for overfitting keep showing up. Most of the time, I would look online for a quick fix and adjust the model as well as the dataset to eliminate such a problem. Mauro and Atsuhiro also provided tremendous help in sorting out the potential mistakes I might make during the experiment. The weekly ROP meeting was super helpful as well because Professor Tyrrell often listens to our follow-ups and gives us valuable suggestions to aid our research experience.
Throughout the entire research experience, there have been frustrations, endeavours and
success. This is overall a wonderful experience for me. I not only learned a lot about Statistics, Machine learning and its implementation in medical imaging, but I also got to know how research is generally being conducted, and most importantly the skills I have acquired throughout the Journey. Thank you for the kind help from the lab members to guide me through such an experience, it is such an intriguing experience!