Summary of Image Synthesis with Class-aware Semantic Diffusion Models For Surgical Scene Segmentation, by Yihang Zhou et al.
Image Synthesis with Class-Aware Semantic Diffusion Models for Surgical Scene Segmentation
by Yihang Zhou, Rebecca Towning, Zaid Awad, Stamatia Giannarou
First submitted to arxiv on: 31 Oct 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 approach to address the challenges of data scarcity and imbalance in surgical scene segmentation. The Class-Aware Semantic Diffusion Model (CASDM) utilizes segmentation maps as conditions for image synthesis, prioritizing critical, less visible classes through class-aware loss functions. CASDM generates realistic image-map pairs, enhancing datasets for training and validating segmentation models. The approach achieves strong effectiveness and generalizability across diverse and challenging datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Surgical scene segmentation is important for making surgery more precise, but it’s hard because there isn’t enough good data to train models. Generative adversarial networks and diffusion models can help create new images, but they don’t do a great job capturing small details that are important. This paper introduces a new way called CASDM that uses the information from segmentation maps to make better images. It also has special loss functions that prioritize capturing those important small details. The approach is tested on different datasets and shows it can create realistic image-map pairs, which helps train and test models. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Image synthesis
