Summary of Synthvision — Harnessing Minimal Input For Maximal Output in Computer Vision Models Using Synthetic Image Data, by Yudara Kularathne et al.
SynthVision – Harnessing Minimal Input for Maximal Output in Computer Vision Models using Synthetic Image data
by Yudara Kularathne, Prathapa Janitha, Sithira Ambepitiya, Thanveer Ahamed, Dinuka Wijesundara, Prarththanan Sothyrajah
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 new approach to rapid development of disease detection computer vision models uses synthetic data generated by diffusion models to train a comprehensive computer vision model for detecting Human Papilloma Virus Genital warts. The two-phase experimental design involves generating a large number of diverse synthetic images from 10 HPV guide images and then training and testing the vision model using this synthetic dataset. The study finds that the vision model trained on synthetic images generated by diffusion models shows exceptional performance in medical image classification, achieving an accuracy rate of 96%, precision of 99% for HPV cases, and recall of 94%. The F1 Score is 96% for HPV cases and 97% for normal cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a computer vision model that can quickly detect Human Papilloma Virus Genital warts using only synthetic data. The researchers use a new method called diffusion models to generate lots of fake images that look like real genital wart pictures. They then train a computer program to recognize these images and test how well it works. The results show that the computer program is very good at recognizing genital warts, with an accuracy rate of 96%. This means it can correctly identify most cases of genital warts. |
Keywords
* Artificial intelligence * F1 score * Image classification * Precision * Recall * Synthetic data
