RDD-Net: Randomized Joint Data-Feature Augmentation and Deep-Shallow Feature Fusion Networks for Automated Diagnosis of Glaucoma

Glaucoma is an irreversible eye disease that has become the leading cause of human blindness worldwide. In recent years, deep learning shows great potential for computer-aided diagnosis in clinics. However, the diversity in medical image quality and acquisition devices leads to distribution shifts that compromise the generalization performance of deep learning methods. To address this issue, many methods relied on deep feature learning combined with the employment of data-level augmentation or feature-level augmentation, respectively. These methods suffer from the limited search space of feature styles. Previous research indicated that introducing a diverse set of augmentations and domain randomization during training can expand the search space of feature styles. In this paper, we propose a Randomized joint Data-feature augmentation and Deep-shallow feature fusion method for automated diagnosis of glaucoma (RDD-Net). It consists of three main components: Data/Feature-level Augmentation (DFA), Explicit/Implicit augmentation (EI), and Deep-Shallow feature fusion (DS). DFA randomly selects data/feature-level augmentation statistics from a uniform distribution. EI involves both explicit augmentation, perturbing the style of the source domain data, and implicit augmentation, utilizing moments information. The randomized selection of different augmentation strategies broadens the diversity of feature styles. DS integrates deep-shallow features within the backbone. Extensive experiments have shown that RDD-Net achieves the SOTA effectiveness and generalization ability. The code is available at https://github.com/TangYilin610/RDD-Net