Regional context-based recalibration network for cataract recognition in AS-OCT

Deep convolutional neural networks (CNNs) have been widely applied to cataract recognition tasks and achieved promising results. However, most existing methods focused on designing data-driven CNN architectures, and failed to exploit asymmetric opacity distribution prior of cataract, which is significant for cataract diagnosis. To this end, this paper proposes a regional context-based recalibration (RCR) module, which fully leverages the clinical prior to recalibrate the feature maps with regional pooling, region-based context integration, and integrated context fusion. We stack these RCR modules to form an RCRNet based on anterior segment optical coherence tomography (AS-OCT) images for cataract recognition. Experiments on the AS-OCT-NC2 dataset and two publicly available medical datasets demonstrate that RCRNet achieves a better trade-off between performance and efficiency than state-of-the-art channel attention-based networks. We also explain the inherent behavior of RCRNet with the aid of the visual analysis. In addition, this paper is the first to study the effects of two performance evaluation methods on AS-OCT image-based cataract classification results: the single-image level and the single-eye level, suggesting that adopting the single-eye level to evaluate cataract classification performance according to clinical diagnosis requirement.