Mixed pyramid attention network for nuclear cataract classification based on anterior segment OCT images

Nuclear cataract (NC) is a leading ocular disease globally for blindness and vision impairment. NC patients can improve their vision through cataract surgery or slow the opacity development with early intervention. Anterior segment optical coherence tomography (AS-OCT) image is an emerging ophthalmic image type, which can clearly observe the whole lens structure. Recently, clinicians have been increasingly studying the correlation between NC severity levels and clinical features from the nucleus region on AS-OCT images, and the results suggested the correlation is strong. However, automatic NC classification research based on AS-OCT images has rarely been studied. This paper presents a novel mixed pyramid attention network (MPANet) to classify NC severity levels on AS-OCT images automatically. In the MPANet, we design a novel mixed pyramid attention (MPA) block, which first applies the group convolution method to enhance the feature representation difference of feature maps and then construct a mixed pyramid pooling structure to extract local-global feature representations and different feature representation types simultaneously. We conduct extensive experiments on a clinical AS-OCT image dataset and a public OCT dataset to evaluate the effectiveness of our method. The results demonstrate that our method achieves competitive classification performance through comparisons to state-of-the-art methods and previous works. Moreover, this paper also uses the class activation mapping (CAM) technique to improve our method's interpretability of classification results.