Summary of Cancer-net Sca-synth: An Open Access Synthetically Generated 2d Skin Lesion Dataset For Skin Cancer Classification, by Chi-en Amy Tai et al.
Cancer-Net SCa-Synth: An Open Access Synthetically Generated 2D Skin Lesion Dataset for Skin Cancer Classification
by Chi-en Amy Tai, Oustan Ding, Alexander Wong
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 presents a new dataset, Cancer-Net SCa-Synth, which utilizes generative artificial intelligence models to synthetically generate 2D skin lesion images. This dataset aims to address class imbalances in current open-source datasets, allowing for more accurate detection of skin cancer using deep learning models. The authors leverage advancements in generative AI, such as Stable Diffusion and DreamBooth, to create high-quality synthetic data that can enhance the performance of machine learning models. By comparing the ISIC 2020 test set performance with and without synthetic images, the study demonstrates the benefits of leveraging synthetic data for improving model accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new dataset that helps doctors better detect skin cancer. The dataset uses special computer programs to make fake pictures of skin lesions, which can help fix some problems in current datasets. This makes it easier for computers to learn and get good at detecting skin cancer. The study shows how this new dataset can help improve the accuracy of machine learning models. |
Keywords
» Artificial intelligence » Deep learning » Diffusion » Machine learning » Synthetic data
