k-SALSA: k-anonymous synthetic averaging of retinal images via local style alignment.

ABSTRACT: 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 personallyidentifying 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

SUBMITTER: Jeon M 

PROVIDER: S-EPMC10388376 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature