Developing computer vision models for detecting visual symptoms of emerging diseases is hampered by the need for large datasets, a significant challenge in rapid response situations like pandemics or bioterrorism. Traditional data collection is time-consuming and insufficient. SynthVision addresses this by using diffusion models to generate synthetic medical images from minimal guide images. While previous work utilized GANs for synthetic image generation, they suffer from limitations in diversity and require large datasets. Diffusion probabilistic models offer advantages in generating high-quality, diverse images, even from limited input, as demonstrated by recent advancements in Denoising Diffusion Probabilistic Models (DDPMs). This study aims to demonstrate the effectiveness of diffusion models for synthetic image generation in healthcare to overcome data scarcity and improve diagnostic accuracy in rapidly evolving medical challenges.

The existing literature highlights the potential and limitations of using GANs and diffusion models for generating synthetic medical images. Studies like Aljohani & Alharbe (2022) showed Deep Pix2Pix GAN's potential in creating realistic medical images. However, GANs are known for instability and lack of diversity. Khader et al. (2022) demonstrated the successful use of diffusion models in synthesizing high-quality medical imaging data, improving model performance with scarce data. Nichol and Dhariwal (2021) advanced DDPM capabilities, allowing for efficient high-quality image generation. Ceritli et al. (2023) also showcased diffusion models' versatility in generating realistic mixed-type Electronic Health Records. These studies provide a foundation for SynthVision's approach, addressing the limitations of previous methods and leveraging the strengths of diffusion models.

SynthVision employed a two-phase methodology. Phase 1 focused on fine-tuning diffusion models for generating images of HPV genital warts. A four-step process was used: 1) Personalization of a Stable Diffusion 1.5 model using DreamBooth, with 10 clinically validated guide images and detailed text prompts; 2) Super-resolution fine-tuning using low- and high-resolution image pairs to preserve detail; 3) Model adjustment based on initial trial feedback, focusing on accurate wart depiction rather than the entire genital area; 4) Training with specific parameters (UNet training steps: 2000; UNet learning rate: 2e-6; Text Encoder training steps: 350; Text Encoder learning rate: 4e-7; Image Resolution: 512) on a Tesla P100 GPU. This generated 630 images, of which 500 were clinically validated and used in the final dataset. Phase 2 involved developing a computer vision model using the generated synthetic dataset. A ViT-Base-Patch16-224 Vision Transformer with attention dropout was used. Data was split into training (500 synthetic HPV, 500 real normal), validation (50 each of HPV and normal), and testing (70 each of HPV and normal) sets. Hyperparameter tuning was performed, with final settings of IMAGE_SIZE = 224x224, BATCH_SIZE = 64, EPOCHS = 150, LEARNING_RATE = 1e-4, and the RMSprop optimizer.

The model trained on the synthetic dataset demonstrated exceptional performance. The confusion matrix showed high true positive and true negative rates, with 66 out of 70 HPV cases correctly identified and all 70 normal cases correctly identified. The classification report indicated perfect precision (1.00) for HPV detection, 95% precision for normal cases, 94% recall for HPV cases, and 100% recall for normal cases. The F1-score was 0.97 for both classes, and the overall accuracy was 97%. The ROC curve analysis yielded an AUC of 0.993, showcasing strong discriminative power. These results highlight the model's high accuracy, specificity, and sensitivity in classifying HPV genital warts and normal cases, suggesting its potential clinical utility.

The study successfully demonstrated the hypothesis that minimal real images can be used to create a large synthetic dataset for training a high-performing disease detection model. This addresses the crucial bottleneck of data scarcity in rapidly emerging medical emergencies. The high performance of the model, comparable to or exceeding models trained on large real datasets, underscores the effectiveness of the SynthVision methodology. The DreamBooth training method and super-resolution fine-tuning were essential for producing high-quality and clinically relevant synthetic images. The rapid generation of training data enables quicker development and deployment of vision models, vital for managing outbreaks. The method's adaptability to new conditions by fine-tuning with a small number of new condition images is also a significant advantage.

SynthVision presents a groundbreaking approach to rapidly developing accurate computer vision models for disease detection using minimal real-world data. The high performance achieved using solely synthetic data generated from diffusion models highlights its potential for deployment in critical situations. Future research should focus on extending this to other medical conditions, improving model efficiency and reducing computational costs, and exploring applications in different imaging modalities.

While the study demonstrates promising results, the reliance on a relatively small number of initial guide images and the potential limitations in generalizability due to the use of a completely synthetic training set should be noted. Further validation on independent, diverse datasets in real-world clinical settings is necessary to fully establish the model's robustness and general applicability.