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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 3·5 min read
InstEditSeg: Instruction-Driven Image Editing for Polyp and Skin Lesion Segmentation
TL;DR
InstEditSeg is a novel generative framework for medical image segmentation that transforms the task into an instruction-driven image editing problem. It achieves competitive accuracy in segmenting polyps and skin lesions while enhancing cross-domain generalization and task control.
✦ Why It Matters
Explore InstEditSeg to enhance your medical image segmentation projects with instruction-driven capabilities.
Key Takeaways
- Utilize InstEditSeg for improved medical image segmentation tasks.
- Leverage instruction-driven editing for better task control.
- Incorporate DINOv3 for enhanced anatomical structure recovery.
- Be aware of color sensitivity in segmentation outputs.
How It Works
InstEditSeg reformulates segmentation as an instruction-driven image editing problem, using a color-coded overlay to represent segmented areas. It employs DINOv3 as an auxiliary encoder to capture fine anatomical details and integrates a multi-scale feature pyramid into a diffusion U-Net model.
This allows the model to leverage visual priors from large-scale pretrained generative models while maintaining the integrity of the original weights.