Skin lesion classification based on hybrid self‐supervised pretext task

Abstract

AbstractThe combination of observation of skin lesion and digital image technology contributes to the diagnosis and treatment of skin diseases. To solve the problems of large variation of target size and shape in skin disease images, small difference between disease images and normal images, and difficulty of label acquisition, we propose a classification algorithm for skin lesion based on hybrid self‐supervised pretext tasks. We combine the discriminative self‐supervised relational reasoning task with the generative self‐supervised mutual information maximization task and design the loss function. Experiments show that the generative‐discriminative self‐supervised co‐training algorithm can identify images with small differences and learn discriminative features and achieve a classification accuracy of 82.6% on the DermaMNIST dataset.