SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation in Melanoma Diagnosis
PositiveArtificial Intelligence
- A new benchmark named SkinGenBench has been introduced to explore the interplay between preprocessing complexity and generative model selection for synthetic dermoscopic image augmentation in melanoma diagnosis. This study utilized a dataset of 14,116 dermoscopic images from HAM10000 and MILK10K, evaluating generative models like StyleGAN2-ADA and Denoising Diffusion Probabilistic Models (DDPMs) under various augmentation techniques.
- The findings indicate that the choice of generative architecture significantly impacts both image quality and diagnostic effectiveness, suggesting that advancements in generative modeling can enhance melanoma detection capabilities. This is crucial for improving diagnostic accuracy in dermatology, where early detection of melanoma can save lives.
- The research aligns with ongoing developments in artificial intelligence for medical imaging, particularly the use of generative adversarial networks and diffusion models to augment training datasets. These innovations are part of a broader trend towards leveraging AI to address challenges in medical diagnostics, enhancing classifier performance and potentially reducing biases in image interpretation.
— via World Pulse Now AI Editorial System
