面向白内障识别的临床特征校准注意力网络

In recent years, convolutional neural networks (CNNs) have been widely used for automatic age-related cataract classification. However, incorporating clinical prior knowledge of age-related cataracts into CNN design improves the classification performance and the interpretability of the decision-making process of CNNs, which has been less studied. To this problem, this paper proposes a clinical-feature recalibration attention network (CFANet) to classify age-related cataract severity levels automatically. In the CFANet, a simple yet effective clinical feature recalibration attention (CFA) block is designed to fuse clinical features adaptively by setting relative weights, aiming to highlight significant channels and suppress redundant ones. This paper conducts extensive experiments on a clinical AS-OCT image dataset of nuclear cataract and a public eye image dataset to verify the effectiveness of CFANet. The results show that CFANet outperforms advanced baselines by above 3.54 percentage points of accuracy on the clinical AS-OCT image dataset, such as squeeze- and-excitation network (SENet), efficient channel network (ECANet), style-based recalibration module (SRM). And the results on the public eye dataset also show that compared with strong attention-based CNNs and published works, proposed method obtains over 1 percentage point improvement. Moreover, this paper also uses visualization methods to analyze clinical feature weights and channel attention weights to enhance the interpretability of the decision-making process for proposed method.