Summary of Ccis-diff: a Generative Model with Stable Diffusion Prior For Controlled Colonoscopy Image Synthesis, by Yifan Xie et al.
CCIS-Diff: A Generative Model with Stable Diffusion Prior for Controlled Colonoscopy Image Synthesis
by Yifan Xie, Jingge Wang, Tao Feng, Fei Ma, Yang Li
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel generative model called CCIS-DIFF (Controlled Colonoscopy Image Synthesis based on Diffusion architecture) to address the challenges in developing robust models for polyp detection in colonoscopies. The existing methods suffer from instability, insufficient data diversity, and lack of precise control over the generation process, resulting in images that fail to meet clinical quality standards. CCIS-DIFF offers precise control over both spatial attributes (polyp location and shape) and clinical characteristics of polyps that align with clinical descriptions. The method introduces a blur mask weighting strategy and a text-aware attention mechanism to generate high-quality colonoscopy images that reflect clinical characteristics. A new multi-modal colonoscopy dataset is constructed, integrating images, mask annotations, and corresponding clinical text descriptions. Experimental results demonstrate the effectiveness of CCIS-DIFF in generating diverse, fine-grained colonoscopy images with controlled spatial constraints and clinical consistency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CCIS-DIFF is a new way to create fake colonoscopy images that can help doctors find hidden growths (polyps) earlier. Doctors use colonoscopies to look for polyps, but making good pictures of the insides of people’s bodies is hard because there aren’t many real pictures to learn from. This makes it difficult to train computers to recognize polyps accurately. The CCIS-DIFF method creates fake images that can be used to train computers and even helps doctors see what they might find in a colonoscopy. |
Keywords
» Artificial intelligence » Attention » Diffusion » Generative model » Image synthesis » Mask » Multi modal
