are very useful because they help clinicians determine things about subjects tied to diseases such as Alzheimer’s. You can use a UniverSeg network to do automatic segmentations because you can provide a context of similar examples without retraining, letting you perform diagnoses much faster. Maybe we’re a step before in the pipeline, but there’s some clinical use.” Adrian, an Assistant Professor at Harvard Medical School, a Research Scientist at MIT, and a long-time friend of our magazine, points out there is a distinction between clinical researchers and clinicians here. “Clinicians may be a little further just because they have all these limitations of what they’re allowed to use and whatnot,” he adds. “For clinical researchers, we’re maybe one step removed, but with improvement, I think we can work with our collaborators to use this.” UniverSeg has laid a strong foundation for further research in context-driven segmentation models. While the model primarily focuses on binary segmentations and 2D slices, there’s immense potential for expansion. The team has already begun work on extending the model to multi-class segmentation and 3D data, addressing the complexities of a wide variety of medical scans. “We’ve built an incredibly important foundation,” Victor asserts. “But there are many directions we would like to go such that it is more applicable.” The model presents a promising research direction for young scholars. One of the team’s most significant challenges was amassing a large and diverse data collection to ensure the model generalized across different datasets and anatomies. As they added more datasets, the model kept improving, consistent with what has been seen in the general AI space and the progress of popular models like ChatGPT. “The literature has focused for a long time on the model aspects and not as much on the data,” Jose tells us. “I would encourage researchers to scale up the data 10 DAILY ICCV Friday Poster Presentation
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