Computer Vision News Computer Vision News 36 Another major contribution is that this unified framework is fully adapted at test time for zero-shot organ segmentation, meaning no training is involved. Traditional methods of adapting SAM to medical imaging involve finetuning or transfer learning, which require substantial computational resources and large datasets. A common challenge in medical imaging is the scarcity of available data. A trainingfree approach bypasses the need for annotated data or human experts and resolves privacy concerns and resource limitations in medical imaging. Harnessing the capabilities of LLMs also eliminates the need for domain expertise in prompt engineering. As our interview draws to a close, Sidra shares some highlights from her recent experience at the International Symposium on Biomedical Imaging (ISBI) in Athens. “I’ve been to a lot of conferences, but there were a few things I experienced for the first time at ISBI,” she says. “The highlight for me was the lunch with leaders, where you sit at a table with a specific leader and speak one-on-one about your career. As I’m nearing the completion of my PhD, I need professional guidance from someone in this field.” Workshop Presentation
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