Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification...

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Computer Vision

Background:

• Limited labeled datasets pose challenges for training robust machine learning models in dermatology.
• Synthetic data generation is crucial for augmenting real-world datasets and improving model generalization.

Purpose of the Study:

• To explore the use of dermatoscopic synthetic data generated by stable diffusion models for enhancing machine learning model robustness.
• To investigate the impact of synthetic data on the performance and adaptability of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs).

Main Methods:

• Utilized stable diffusion models and few-shot learning for text-to-image generation of synthetic dermatoscopic data.
• Incorporated enhanced data transformation techniques to create diverse and realistic skin lesion images.
• Trained and evaluated state-of-the-art machine learning models (CNNs, ViTs) using both real and synthetic datasets.

Main Results:

• Synthetic data generated via stable diffusion models significantly improved the robustness and adaptability of CNN and ViT models.
• The enhanced training pipeline demonstrated better generalization capabilities on unseen real-world skin cancer datasets.
• The study successfully generated high-quality, diverse synthetic skin lesion data.

Conclusions:

• Stable diffusion-generated synthetic dermatoscopic data is an effective strategy for improving machine learning model performance in skin cancer detection.
• The open-sourced dataset and model facilitate further research and development in AI-driven dermatology.
• Synthetic data generation offers a viable solution to data scarcity in medical imaging applications.