Summary of Synthetic Generation Of Dermatoscopic Images with Gan and Closed-form Factorization, by Rohan Reddy Mekala et al.
Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization
by Rohan Reddy Mekala, Frederik Pahde, Simon Baur, Sneha Chandrashekar, Madeline Diep, Markus Wenzel, Eric L. Wisotzky, Galip Ümit Yolcu, Sebastian Lapuschkin, Jackie Ma, Peter Eisert, Mikael Lindvall, Adam Porter, Wojciech Samek
First submitted to arxiv on: 7 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 research proposes an innovative unsupervised augmentation method to generate high-quality dermatoscopic images, overcoming the limitations of existing annotated datasets. The approach uses Generative Adversarial Network (GAN) based models to create controlled semantic variations in skin lesion images. By augmenting training data with these synthetic images, machine learning models can be improved and achieve better performance on skin lesion classification tasks, such as the HAM10000 dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper helps machines learn better from skin image pictures by creating fake but realistic images to train them. This makes it easier for computers to recognize skin problems early and accurately. The research uses special computer models (GANs) to make these fake images and shows that they can improve the performance of machine learning models. |
Keywords
» Artificial intelligence » Classification » Gan » Generative adversarial network » Machine learning » Unsupervised
