Diana Escoboza’s ESC499 Journey

Hello there! My name is Diana Escoboza, and I’ve just finished my undergraduate studies at UofT in Machine Intelligence Engineering. I am very fortunate to have Prof. Tyrell as my supervisor while I worked on my engineering undergraduate thesis project ESC499 during the summer. I believe such an experience is worth sharing!

My project consisted of training an algorithm to identify/detect the anatomical landmarks on ultrasounds for the elbow, knee, and ankle joints. In medical imaging, it is challenging to correctly label large amounts of data since we require experts, and their time is minimal and costly. For this reason, I wanted my project to compare the performance of different machine learning approaches when we have limited labelled data for training.

The approaches I worked on were reinforcement and semi-supervised learning. Reinforcement learning is based on learning optimal behaviour in an environment through decision-making. In this method, the model would ‘see’ a section of the image and choose a direction to move towards the target landmark. In semi-supervised learning, both labelled and unlabelled data are used for training, and it consists of feeding the entire image to the model for it to learn the target’s location. Finally, I analysed the performance of both architectures and the training resources used to determine the optimal architecture.

While working on my project, I sometimes got lost in the enthusiasm and possibilities and overestimated the time I had. Prof. Tyrell was always very helpful in advising me throughout my progress to keep myself sensible on the limited time and resources I had while still giving me the freedom to work on my interests. The team meetings not only provided help, but they were also a time we would talk about AI research and have interesting discussions that would excite us for our projects and future possibilities. We also had a lot of support from the grad students in the lab, providing us with great help when encountering obstacles. A big shout-out to Mauro for saving me when I was freaking out my code wasn’t working, and time was running out.

Overall, I am very grateful for having the opportunity to work with such a supportive team and for everything I learned along the way. With Prof. Tyrell, I gained a better understanding of scientific research and advanced my studies in machine learning. I want to thank the MiData team for all the help and for providing me with such a welcoming environment.

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