  Despite continued advancement in recent years, deep neural networks still
rely on large amounts of training data to avoid overfitting. However, labeled
training data for real-world applications such as healthcare is limited and
difficult to access given longstanding privacy, and strict data sharing
policies. By manipulating image datasets in the pixel or feature space,
existing data augmentation techniques represent one of the effective ways to
improve the quantity and diversity of training data. Here, we look to advance
augmentation techniques by building upon the emerging success of text-to-image
diffusion probabilistic models in augmenting the training samples of our
macroscopic skin disease dataset. We do so by enabling fine-grained control of
the image generation process via input text prompts. We demonstrate that this
generative data augmentation approach successfully maintains a similar
classification accuracy of the visual classifier even when trained on a fully
synthetic skin disease dataset. Similar to recent applications of generative
models, our study suggests that diffusion models are indeed effective in
generating high-quality skin images that do not sacrifice the classifier
performance, and can improve the augmentation of training datasets after
curation.

Machine Learning;Computer Vision and Pattern Recognition;Image and Video Processing