CCA-Net: Clinical-awareness attention network for nuclear cataract classification in AS-OCT

Nuclear cataract (NC) is the leading cause of vision impairment and blindness globally. NC patients can slow the opacity development with early intervention or recover vision with cataract surgery. Anterior segment optical coherence tomography (AS-OCT) images have been increasingly used for clinical NC diagnosis. Compared with other ophthalmic images, e.g., slit lamp images, AS-OCT images are vital for NC diagnosis due to their capability of clearly capturing the nucleus region. Moreover, clinical research has shown the high correlation and repeatability between NC severity levels and image features like mean, maximum, and standard deviation on AS-OCT images. This paper aims to incorporate the clinical features into convolutional neural networks (CNNs) to improve NC classification results and enhance the interpretation of the decision process. Thus, we propose a novel clinical-awareness attention network (CCA-Net) to classify NC severity levels automatically. In CCA-Net, we design a practical yet effective clinical-aware attention block, which not only uses the mixed pooling operator to extract clinical features from each channel but also applies the designed clinical integration operator to focus on salient channels. We conduct extensive experiments on one clinical AS-OCT image dataset and two publicly available ophthalmology datasets. The results demonstrate that the CCA-Net outperforms state-of-the-art attention-based CNNs and strong baselines. Moreover, we also provide in-depth analysis to explain the internal behaviors of our method, enhancing the interpretation ability of our method.