18 DAILY MICCAI Tuesday Tutorial Preview by Camila González Have you ever worked with data from five, or even ten years ago? You probably noticed how different it is from more recent cases. If you didnot, your model definitely did. Many state-of-the-art methods for medical imaging rely on deep learning models that are susceptible to distribution shifts. Several factors cause changes in data acquisition, including ever-evolving scanning technologies and the presence of image artefacts. Likewise, naturally occurring shifts in disease expression and spread can cause the annotated training base to become outdated. As a result, deep learning models deteriorate over time until they are no longer helpful to the clinician. To maintain the expected performance, models must adapt to incorporate new data patterns while preserving their proficiency in the original evaluation set. Continual learning allows us to acquire new information without losing previous knowledge. This opens up attractive possibilities, such as extending the lifespan of medical software solutions and leveraging large amounts of multi-institutional data. I am Camila González, a postdoctoral researcher working at the Computational Neuroscience Laboratory at Stanford University, School of Medicine. Since my undergrad days, I have been passionate about developing deep learning approaches that translate well to dynamic clinical settings. I am excited to be organizing the first MICCAI tutorial on Dynamic AI in the Clinical Open World (DAICOW) together with a wonderful team of colleagues, to be held in conjunction with MICCAI 2023 on the morning of October 12th (starting at 8 am, but don’t shy away if you can’t make the earlycall ). DAICOW @ MICCAI2023
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