Label-Preserving Data Augmentation in Latent Space for Diabetic Retinopathy Recognition

Abstract

AI based methods have achieved considerable performance in screening for common retinal diseases using fundus images, particularly in the detection of Diabetic Retinopathy (DR). However, these methods rely heavily on large amounts of data, which is challenging to obtain due to limited access to medical data that complies with medical data protection legislation. One of the crucial aspects to improve performance of the AI model is using data augmentation strategy on public datasets. However, standard data augmentation methods do not keep the labels. This paper presents a label-preserving data augmentation method for DR detection using latent space manipulation. The proposed approach involves computing the contribution score of each latent code to the lesions in fundus images, and manipulating the lesion of real fundus images based on the latent code with the highest contribution score. This allows for a more targeted and effective label-preserving data augmentation approach for DR detection tasks, which is especially useful given the imbalanced classes and limited available data. The experiments in our study include two tasks, DR classification and DR grading, with 4000 and 2000 labeled images in their training sets, respectively. The results of our experiments demonstrate that our data augmentation method was able to achieve a 6% increase in accuracy for the DR classification task, and a 4% increase in accuracy for the DR grading task without any further optimization of the model architectures.