Summary of Derm-t2im: Harnessing Synthetic Skin Lesion Data Via Stable Diffusion Models For Enhanced Skin Disease Classification Using Vit and Cnn, by Muhammad Ali Farooq et al.
Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification using ViT and CNN
by Muhammad Ali Farooq, Wang Yao, Michael Schukat, Mark A Little, Peter Corcoran
First submitted to arxiv on: 10 Jan 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 The paper explores the use of dermatoscopic synthetic data generated using stable diffusion models to enhance the robustness of machine learning model training. By leveraging few-shot learning and text-to-image latent diffusion models, the study generates high-quality skin lesion synthetic data with diverse characteristics, supplementing existing training datasets. The impact of incorporating this synthetic data into state-of-the-art machine learning models is assessed, demonstrating its effectiveness in improving model performance and generalization on real-world skin lesion datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at using fake skin images made by computers to help train machines to recognize skin lesions better. They use special computer programs to make these fake images that look like real ones. Then they test how well the machine learning models do when they’re trained with both real and fake images. The results show that this helps the models work better on new, unseen images. |
Keywords
» Artificial intelligence » Few shot » Generalization » Machine learning » Synthetic data
