Summary of S-synth: Knowledge-based, Synthetic Generation Of Skin Images, by Andrea Kim et al.
S-SYNTH: Knowledge-Based, Synthetic Generation of Skin Images
by Andrea Kim, Niloufar Saharkhiz, Elena Sizikova, Miguel Lago, Berkman Sahiner, Jana Delfino, Aldo Badano
First submitted to arxiv on: 31 Jul 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 S-SYNTH, a novel open-source framework for generating synthetic skin datasets. The framework simulates skin appearance, allowing for controlled variation in parameters like skin color, hair presence, lesion shape, and blood fraction. This adaptable tool is designed to help develop and evaluate AI models for skin lesion segmentation by mitigating biases from existing datasets. S-SYNTH can generate synthetic 3D models, digitally rendered images, and skin textures, mimicking real dermatologic images. By studying the effect of variations on AI model performance, this framework aims to improve training and evaluation in medical imaging, particularly in dermatology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special computer program called S-SYNTH that makes fake pictures of human skin. The program can make different kinds of skin, like different colors or shapes of moles. This helps scientists develop better computers that can recognize skin problems. The fake pictures are useful because they fix some problems with real pictures, like having too few examples or not being diverse enough. |
