  The application of modern machine learning to retinal image analyses offers
valuable insights into a broad range of human health conditions beyond
ophthalmic diseases. Additionally, data sharing is key to fully realizing the
potential of machine learning models by providing a rich and diverse collection
of training data. However, the personally-identifying nature of retinal images,
encompassing the unique vascular structure of each individual, often prevents
this data from being shared openly. While prior works have explored image
de-identification strategies based on synthetic averaging of images in other
domains (e.g. facial images), existing techniques face difficulty in preserving
both privacy and clinical utility in retinal images, as we demonstrate in our
work. We therefore introduce k-SALSA, a generative adversarial network
(GAN)-based framework for synthesizing retinal fundus images that summarize a
given private dataset while satisfying the privacy notion of k-anonymity.
k-SALSA brings together state-of-the-art techniques for training and inverting
GANs to achieve practical performance on retinal images. Furthermore, k-SALSA
leverages a new technique, called local style alignment, to generate a
synthetic average that maximizes the retention of fine-grain visual patterns in
the source images, thus improving the clinical utility of the generated images.
On two benchmark datasets of diabetic retinopathy (EyePACS and APTOS), we
demonstrate our improvement upon existing methods with respect to image
fidelity, classification performance, and mitigation of membership inference
attacks. Our work represents a step toward broader sharing of retinal images
for scientific collaboration. Code is available at
https://github.com/hcholab/k-salsa.
