Hi everyone! My name is Phoebe (Shih-Hsin) Chuang, and I’m a third-year Computer Science Specialist student with a minor in Statistics and a focus in Artificial Intelligence. This year, I had the opportunity to work on my first formal research project involving machine learning in the field of medical imaging. Although the experience was often stressful and full of challenges, it has definitely been one of the most meaningful and transformative learning experiences of my undergraduate academic journey so far.
Before starting this ROP, I had no prior experience in either machine learning or medical imaging. Choosing a research topic initially felt overwhelming. Formulating a good research question required a deep understanding of the current state of the field, so I spent a great deal of time reading papers to grasp major trends such as image generation, multimodal learning, image segmentation, and classification tasks. Eventually, I decided to focus on adnexal mass classification using ultrasound images from the lab.
A major challenge for this project was the small dataset size compared to those typically used in current literature. Recognizing this limitation, I explored approaches specifically designed for small data scenarios. I found that radiomics was particularly promising, especially given that deep learning models typically require large datasets to generalize well. To make my approach more nuanced, I chose not just to use extracted radiomics features in numeric form, but to generate radiomic feature maps. This allowed me to integrate them directly into convolutional neural networks, leveraging CNNs’ strengths in learning from images.
Although this may appear minor, aside from selecting the research topic and technical exploration, one of the biggest lessons I learned was the importance of keeping my code, folders, and documentation organized. Without a clear structure from the beginning, it became very easy to get lost, especially when I paused work for a few days. If I could redo the project, I would definitely prioritize setting up a consistent, organized structure early on to save a lot of confusion and debugging time later.
Looking back, I am deeply grateful to Dr. Tyrrell for offering me this invaluable research opportunity. Through weekly meetings, Dr. Tyrrell emphasized that the primary goal of this experience was not simply achieving great results, but learning the full research process, from identifying gaps in knowledge to formulating research questions and hypotheses, designing experiments, and performing rigorous statistical analyses (since this was a statistics department course!). I would also like to sincerely thank Noushin, our postdoc, whose insightful feedback and support helped me greatly in refining my research questions and overcoming challenges during implementation. Finally, I want to thank everyone else in the lab for their encouragement, shared experiences, and thoughtful suggestions during meetings. It was both inspiring and motivating to see everyone’s projects evolve alongside mine.
This ROP journey has definitely been a steep but rewarding learning curve. It has brought me one step closer to becoming an independent researcher, and I look forward to carrying the skills, mindset, and resilience I built this year into my future research and career endeavours.