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Textual input can drastically alter segmentation outcomes, with some datasets showing catastrophic performance drops when text is removed, while others remain largely unaffected.
Instruction-driven image editing can outperform traditional binary segmentation methods in medical imaging, achieving better generalization and flexibility.