Hi! I’m Xin Lei! I was a second-year Computer Science Specialist and Molecular Genetics major student when I began my ROP with Professor Tyrrell.
My project focused on developing a framework that uses Latent Diffusion Models (LDMs) to generate high-fidelity gastrointestinal (GI) medical images from segmentation masks.
I trained a two-stage pipeline: first, a VQ-GAN model to encode the structure of unlabeled GI images into a latent space and then conditioned a Latent Diffusion Model on segmentation masks to generate corresponding realistic GI tract images. To enhance anatomical diversity, I also designed a novel mask interpolation pipeline to create intermediate anatomical configurations, encouraging the generation of diverse and realistic segmentation-image pairs. It was challenging to tackle the challenge of synthesizing new, varied, and coherent medical images for segmentation tasks, and to push beyond the limitations of existing inpainting and stitching-based generation methods.
Overall, it was a lot of paper reading, GitHub repositories visited, and overnight coding session, all of which would have been impossible without Professor Tyrrell’s continual support and advice! My biggest mistake was not spending enough time reading about the best current methods for solving my problem of interest. Indeed, countless hours would have been saved, if I had found the right repositories and research papers earlier, where others had already implemented parts of the ideas I was trying to build!
Reflecting on my ROP journey, the most difficult part was avoiding the endless rabbit holes of technical optimizations. I would often find myself spending days obsessing over marginal model improvements, investigating every possible architectural tweak or hyperparameter adjustment I could think of. While these deep dives were fun and intellectually stimulating, they were dangerous because no project could ever be delivered on time if perfection was the only goal.
I owe a huge thanks to Professor Tyrrell, who repeatedly pulled me back out of these tangents and helped me refocus on moving the project forward. His guidance taught me one of the most valuable lessons of research: perfect is the enemy of good. A deliverable, working project is far more valuable than an imaginary, flawless one stuck in perpetual revision.
In the end, I am proud of what I accomplished, not just technically, but also in learning how to think more strategically about research. This experience has cemented my excitement about applying AI to real-world medical problems, and I am deeply grateful to Professor Tyrrell and the MiDATA lab for giving me this incredible opportunity.
I can’t wait to see where this journey will take me next!
Xin Lei Lin