SynthVision -- Harnessing Minimal Input for Maximal Output in Computer Vision Models using Synthetic Image data

TL;DR

This paper presents a novel approach using diffusion models to generate synthetic medical images for training computer vision models, achieving high accuracy in detecting HPV genital warts with minimal real data.

Contribution

The study introduces a two-phase method employing diffusion models to generate synthetic images and train effective medical image recognition models from minimal data.

Findings

Achieved 96% accuracy in detecting genital warts

High precision of 99% and recall of 94% for HPV cases

F1 Score of 96% for HPV and 97% for normal cases

Abstract

Rapid development of disease detection computer vision models is vital in response to urgent medical crises like epidemics or events of bioterrorism. However, traditional data gathering methods are too slow for these scenarios necessitating innovative approaches to generate reliable models quickly from minimal data. We demonstrate our new approach by building a comprehensive computer vision model for detecting Human Papilloma Virus Genital warts using only synthetic data. In our study, we employed a two phase experimental design using diffusion models. In the first phase diffusion models were utilized to generate a large number of diverse synthetic images from 10 HPV guide images explicitly focusing on accurately depicting genital warts. The second phase involved the training and testing vision model using this synthetic dataset. This method aimed to assess the effectiveness of diffusion models in rapidly generating high quality training data and the subsequent impact on the vision model performance in medical image recognition. The study findings revealed significant insights into the performance of the vision model trained on synthetic images generated through diffusion models. The vision model showed exceptional performance in accurately identifying cases of genital warts. It achieved an accuracy rate of 96% underscoring its effectiveness in medical image classification. For HPV cases the model demonstrated a high precision of 99% and a recall of 94%. In normal cases the precision was 95% with an impressive recall of 99%. These metrics indicate the model capability to correctly identify true positive cases and minimize false positives. The model achieved an F1 Score of 96% for HPV cases and 97% for normal cases. The high F1 Score across both categories highlights the balanced nature of the model precision and recall ensuring reliability and robustness in its predictions.