DermAIverse is a pioneering global initiative aimed at developing the first comprehensive database of skin lesion images that encompass a diverse range of skin pigmentation. The primary objective of this project is to mitigate the biases in machine learning models by providing a robust and extensive dataset that captures the full spectrum of lesion characteristics, including body location, skin pigmentation, size, and distribution.
One of the cutting-edge techniques employed in this initiative involves the use of diffusion models. These generative models have the capability to produce high-resolution, high-quality images. In the context of DermAIverse, diffusion models are utilized to enhance the dataset by generating augmented images of skin lesions. This augmentation process includes increasing the diversity of skin pigmentation in the images and altering the location of lesions to different parts of the body. By doing so, the models can be trained on a more varied and representative dataset, ultimately leading to more accurate and unbiased diagnostic tools.
In the following images, we demonstrate, as preliminary data, the effectiveness of diffusion models in augmenting lesion images. The models successfully modify the pigmentation of skin images and relocate lesions to various body parts, showcasing their potential to significantly improve the quality and diversity of current datasets. This innovative approach is a crucial step towards developing more inclusive and reliable machine learning models for dermatological applications, ensuring that all skin types are adequately represented and diagnosed with greater accuracy.
