Category: Research Opportunity Program

Hi all! My name is Jingwen (Lisa) Zhong. I’m a Data Science Specialist and Actuarial Science Major at UofT, graduating in 2026. I’m really happy and honored to have joined Prof. Tyrrell’s lab in the summer of 2024 as an ROP299 student. This was my first research project, and it has truly exercised many of my research and scientific skills, such as literature review, critical thinking, and the ability to get familiar with a brand-new field.

Coming into the lab, I had no research experience and no prior knowledge of medical imaging. As a student just finishing my second year of study, I felt curious about machine learning and artificial intelligence because these topics are so widely discussed. However, I still can’t forget how uneasy I felt during the first few weeks as I tried to think of a research question related to medical images and machine learning. I’m incredibly thankful to Prof. Tyrrell, who ‘relentlessly’ pointed out issues during each lab meeting, and to the lab volunteers, Daniel and Atshuhiro, who were always willing to help and guide me through the process. I couldn’t have gotten my project ready for implementation without their support. After a month of struggle, I finally settled on my research topic: investigating whether LPIPS is a better metric for assessing the similarities of medical images compared to PSNR and SSIM under various degradation conditions.

Having a research question is just the beginning; implementing it is another huge mountain to climb. I remember how excited I was when my research question was finally approved. I worked hard that week to implement almost all the code for my project. If I could go back, I would approach this differently. Instead of diving straight into coding, I would first take the time to design the entire study process—splitting the dataset, testing the code on a smaller dataset, figuring out how to use the GPU, then applying the code to the full dataset, and finally choosing the appropriate statistical analysis. I say this because I stumbled at each of these steps. After completing my code, I found that it ran so slowly that it would take several days to get results. So, I began the process of figuring out how to set up the environment to run on the lab’s GPU. This process took me almost two weeks, but with the help of other ROP students, I finally got the code running on the GPU.

Once the GPU problem was solved, my results came in much faster. However, the next obstacle was interpreting these results. As a Data Science student, it’s hard to admit, but I hadn’t yet learned ANOVA. Initially, I turned to ChatGPT for help, but the results weren’t ideal. Prof. Tyrrell suggested that I use SAS to perform ANOVA, which provided me with ideal and comprehensive results. So, I learned how to use SAS—a very powerful statistical analysis tool compared to Python.

Through this ROP experience, I learned the importance of communication and teamwork. Although we worked on different projects, the weekly lab meetings were incredibly helpful. It was a place where everyone’s intelligence came together, and I always left with new insights and a clear plan in mind.

Overall, this journey has been a steep learning curve but an immensely rewarding one. I am grateful for the opportunity to work with such a supportive team, and I know that the skills and lessons I’ve learned will continue to guide me in my future research endeavors.

Hey! I am Mason Hu, a Data Science Specialist and Math Applications in Stats/Probabilities Specialist who just finished my second year. This summer’s ROP journey in MiDATA lab has been an enlightening journey for me, marking my first formal venture into the world of research. Beyond gaining insight into the intricate technicalities of machine learning and medical imaging, I’ve gleaned foundational lessons that shaped my understanding of the research process itself. My experience can be encapsulated in the following three points:

Research is a journey that begins with a wide scope and gradually narrows down to a focused point. When I was writing my project proposal, I had tons of ideas and planned to test multiple hypotheses in a row. Specifically, I envisioned myself investigating four different attention mechanisms of UNet and assessing all the possible combinations of them, which was already discouraged by Prof. Tyrrell in the first meeting. My aspirations proved to be overambitious, as the dynamic nature of research led me to focus on some unexpected yet incredible discoveries. One example of this would be my paradoxical discovery that attention maps in UNets with residual blocks have almost completely opposite weights to those without. Hence, for a long time, I delved into the gradient flows in residual blocks and tried to explain the phenomenon. Even when time is limited and not all ambitious goals can be reached, the pursuit of just one particular aspect can lead to spectacular insights.

Sometimes plotting out the weights and visualizing them gives me the best sparks and intuitions. This is not restricted to visualizing attention maps in this case. The practice of printing out important statistics and milestones in training models might usually yield great fruition. I once printed out each and every one of the segmentation IoUs in a validation data loader, and it surprised me that some of them are really close to zero. I tried to explain this anomaly as model inefficacy, but it just made no sense. Through an intensive debugging session, I came to realize that it is actually a PyTorch bug specific to batch normalization when the batch size is one. As I go deeper and deeper into the research, I get a better and better understanding of the technical aspects of machine learning and discover better what my research objectives and my purpose are.

Making models reproducible is a really hard task, especially when configurations are complicated. In training a machine learning model, especially CNNs, we usually have a dozen tunable hyperparameters, sometimes more. The technicality of keeping track of them and changing them is already annoying, let alone reproducing them. Moreover, changing an implementation to an equivalent form might not always produce completely equivalent results. Two seemingly equivalent implementations of a function might have different implicit triggers of functionalities that are hooked to one but not the other. This can be especially pronounced in optimized libraries like PyTorch, where subtle differences in implementation can lead to significantly divergent outcomes. The complexity of research underscores the importance of meticulous tracking and understanding of every aspect of the model, affirming that reproducibility is a nuanced and demanding facet of machine learning research.

Reflecting on this summer’s research, I am struck by the depth and breadth of the learning that unfolded. I faced a delicate balance between pursuing big ideas and focusing on careful investigation, always keeping an eye on the small details that could lead to surprising insights. Most importantly, thanks to Prof. Tyrrell, Atsuhiro, Mauro, and Rosa for all the feedback and guidance. Together, they formed a comprehensive research experience for me. As I look to the future, I know that these lessons will continue to shape my thinking, guiding my ongoing work and keeping my curiosity alive.

Hello! My name is Lucie Yang, and I am excited to share my experience with my ROP project this summer! I’m heading into my second year, pursuing a Data Science specialist. While I have been interested in statistics for a long time, I was not sure exactly what field to pursue. Over the past year, I became fascinated with machine learning and decided to apply to Prof. Tyrrell’s posting, despite being in my first year and not having any previous experience with machine learning or medical imaging. To my surprise, I was accepted and thus began my difficult, yet incredibly rewarding journey at the lab.

I remember Prof. Tyrrell had warned me during my interview that the research process would be challenging for me, but still, I was excited and confident that I could succeed. The first obstacle I encountered was choosing a research project. Despite spending hours scrolling through lessons on Coursera and YouTube and reading relevant papers to build my understanding, I struggled to come up with a topic that was feasible, novel, and interesting. I would go to the weekly ROP meetings thinking I had come up with a brilliant idea, only to realize that there was some problem that I had not even considered. After finally settling on an adequate project, I was met with another major obstacle: actually implementing it.

My project was about accelerating the assessment of heterogeneity on an X-Ray dataset with Fourier-transformed features. Past work done in the lab had shown that cluster analysis of features extracted from CNN models could indicate dataset heterogeneity, therefore, I wanted to explore whether the same would hold for Fourier-transformed features and whether it would be faster to use them. With the help of a previous student’s code, implementing the CNN pipeline was relatively straightforward; however, I struggled to understand how to apply the Fast Fourier Transform to images and extract the features. As deadlines loomed near and time was quickly ticking away, I was unsure of whether my code was even correct and became very frustrated. Prof. Tyrrell and Mauro gave me immense help, helping me refine my methodology and answering my many questions. After that, I was able to get back on track and thankfully, completed the rest of my project in time.

I learned a lot from this journey, far more than I have in any class I’ve taken, from the exciting state-of-the-art technologies being developed to the process of conducting research and writing code for machine learning. Above all, I gained a deeper appreciation of the bumpy road of research, and I am incredibly grateful to have had the opportunity to get a taste of it. I am very thankful to all the helpful lab members, and I look forward to continuing my journey in data science and research in the coming years!

Lucie Yang

Hi! My name is Christine Wang, and I’m finishing my third year at the University of Toronto pursuing a specialist in statistics with a focus on cognitive psychology. The STA299 journey through the whole year has been a really amazing and challenging experience.

My research project involved assessing whether the heterogeneity of medical images affects the clustering of image features extracted from the CNN model. Initially, I found it quite challenging to understand the difference between my research and the previous work done by Mauro, who analyzed the impact of heterogeneity on the generalizability of CNN by testing the overall model performance on the test clusters. Many thanks to the discussions in the ROP meeting every week, I understood that I needed to retrain the CNN model using the images in each of the clusters in the training set to see how heterogeneity could affect the clustering of image features. By checking whether the retrained CNN models from each cluster perform differently, I was able to show that heterogeneity could affect the clustering of image features. However, the most challenging part of the research is not just achieving the desired results, but rather interpreting what I could learn from those results. For instance, even though I obtained results that showed the retrained models perform differently, I spent a lot of time trying to understand what the clusters represent and why some retrained models perform better than others. I am very grateful to Professor Pascal Tyrrell for helping me understand my project and providing me with essential advice to check the between-cluster distances. This enabled me to interpret the results and identify a possible pattern: the retrained models with similar performance come from clusters that are also close to each other. However, further research is still required because the two datasets I used were not large enough. Looking back, I realize that it would have been better if I used the dataset in our lab, as finding the appropriate dataset and code was very challenging. I would like to thank Mauro, Atshuhiro, and Tristal for their generous help in teaching me how to do feature extraction and cluster analysis.

Before starting the project, I was fascinated by the high accuracy and excellent performance of ML techniques. However, during the ROP journey, I realized that achieving high model performance is not the most important thing. As Professor Pascal mentioned, the most crucial aspect of doing research is truly understanding what we are doing and focusing on interpreting what we can learn from the results we obtain. It is not enough to just have tables and figures; we need to go further by choosing appropriate statistical analysis to understand our results.

Hi friends! My name is Alice Zhang. I am finishing my third year of undergrad pursuing a
statistical science specialist with a focus on genetics and biotechnology, as well as a biology minor. It was a blessing to take part in STA299Y ROP with Professor Tyrrell and his MiDATA lab. As this experience comes to an end, I would like to share about my incredible journey.

Coming into the lab, I held great interest but zero research experience and zero knowledge about machine learning. I remember being completely lost and worried in my very first lab meeting. Looking back, I’m actually quite proud of how far I’ve come. My project was to compare multiple-instance classifiers and single-instance classifiers for diagnosing knee recess distension ultrasounds. I also explored factors that may influence multiple-instance model training.

The start of my project was rather smooth compared to others since it was more application-based than theoretical. I was able to grasp key concepts through literature searches and gather usable models and datasets (thanks to Mauro) needed to begin the project. However, with a lack of research experience and weak background in programming, I soon faced obstacles, confusion, panic and doubts. I had the tools in hand, but the hard part was designing, running and interpreting appropriate experiments. How do I modify and apply the code to my ultrasound data? How do I fairly compare two dissimilar algorithms? How do I unbiasedly alter and compare training factors? How do I give rational interpretations of the outcomes and unusual observations?

As the project progressed, I constantly felt that I was falling behind; I was still doubting and
modifying my experiments while my peers obtained results, I was still training my models while others were starting the write-up. To be honest, I panicked in every ROP meeting, but I was supported by Professor Tyrrell, lab members and my peers. I was able to power through. I am so grateful for having Professor Tyrrell as my guide through the first doorstep of research. He taught me that research isn’t about finding and reporting a standard answer, it is a process of discovering and then solving problems, and there’s no template for it. I was constantly encouraged to reflect on the “what”, “how” and “why” of the process. I also greatly appreciate the help from Mauro, who prepared the dataset and spent many hours guiding me through programming and model training.

Progressing through the project, I was later able to solve problems and modify bugs
independently. I started from zero to now completing my very first research project in machine learning. It feels like I’ve raised my first “research baby”! I would like to once again thank Professor Tyrrell and the lab members for their support, I couldn’t have gained this marvellous learning experience without them.

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.

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!

Hi! My name is Paul Tang and I just finished my second year at UofT studying computer science specialist and cognitive science major. During this summer, I enrolled in STA299 under the supervision of Prof. Pascal Tyrrell to learn how to conduct research, and I will be sharing my experience in this reflection blog post.

The first phase of my ROP experience concerns formulating a research question. Having a keen interest in machine learning, I got my inspiration for combining it with my research from a weekly lab meeting where Mauro presented his graduate research work (on the generation of synthetic ultrasound image data). I decided to focus on the problem that the amount of annotated data in the field of medical imaging is often limited for effective supervised training. Eventually, by reading papers and discussing my ideas with Prof. Tyrrell during the first few weeks, the solution I decided on was to use self supervised learning to pretrain a machine learning model for improving its performance. In particular, I chose the contrastive learning based self supervised learning method called DenseCL. Luckily, I got my data right at the lab using the ultrasound knee recess distension dataset for semantic segmentation. My ROP project dealt with comparing the effect of using DenseCL pretraining on the segmentation performance.

At first, I was doubtful of my research question: afterall, many papers I read already showed using self supervised pretraining did improve task performance, so wouldn’t my research be too “obvious”? However, I realized along the way that some interesting gaps still existed (e.g. current self supervised pretrain methods used in the domain of medical images do not extract local image features, which could be helpful for segmentation tasks), and these gave me confidence and excitement for my research.

Getting to work, I first identified the github repositories I would use in my project. Setting up the environment and the repositories to work with my dataset took much longer than expected (in fact, I had to switch to a different github repository due to “false advertising” from the original one), and I learned that checking with lab members (Mauro, Atsuhiro) and asking for ideas when starting to work on anything could save much needed time. I made several mistakes while training my models. When I first obtained the performance result (mIoU) from my segmentation model, I was relieved that it was consistent with previous results obtained in the lab. However, using this model in another experiment produced highly untypical results, which led me back to debug the model. Eventually the problem was found to be due to small batch size. Although this mistake cost me much training time, it did allow me to explore and gain familiarity with the configurations of a machine learning model, which I find very rewarding.

Eventually, I obtained results that show a small performance improvement in using DenseCL pretraining for the segmentation of ultrasound knee distention images. My project still had its limitations: my result was not statistically rigorous as I didn’t account for randomness in the training process. Furthermore, the amount of images I used for DenseCL pretraining is much fewer than what would typically be used in a self supervised learning setting. These limitations served as great motivation for further research.

This research experience taught me how humbling doing research was: many things I took for granted require careful testing, and that many gaps still exist in the current literature upon closer inspection. I am thankful to Prof. Tyrrell’s openness for allowing us to choose our own research questions, and I am thankful to all the help the lab members (especially Mauro and Atsuhiro) provided to me.

Paul Tang