Towards more efficient ophthalmic disease classification and lesion location via convolution transformer. (June 2022) - Wen, Huajie; Zhao, Jian; Xiang, Shaohua; Lin, Lin; Liu, Chengjian; Wang, Tao; An, Lin; Liang, Lixin; Huang, Bingding
Highlights: Ophthalmic disease analysis using convolutional neural networks and self-attention mechanisms. B-scan images of 4686 adult patients with different ophthalmic disease were selected. Self-supervised lesion localization based on ophthalmic disease classification results. Compared with other methods, our proposed method improves the overall accuracy, sensitivity and specificity by 7.6, 10.9 and 9.2, respectively. Abstract: Objective: A retina optical coherence tomography (OCT) image differs from a traditional image due to its significant speckle noise, irregularity, and inconspicuous features. A conventional deep learning architecture cannot effectively improve the classification accuracy, sensitivity, and specificity of OCT images, and noisy images are not conducive to further diagnosis.  This paper proposes a novel lesion-localization convolution transformer (LLCT) method, which combines both convolution and self-attention to classify ophthalmic diseases more accurately and localize the lesions in retina OCT images. Methods: A novel architecture design is accomplished through applying customized feature maps generated by convolutional neutral network (CNN) as the input sequence of self-attention network. This design takes advantages of CNN's extracting image features and transformer's consideration of global context and dynamic attention. Part of the model is backward propagated to calculate the gradient as a weight parameter, which is multiplied and summed with the global features generated by the forward propagation process to locate the lesion. Results: Extensive experiments show that our proposed design achieves improvement of about 7.6% in overall accuracy, 10.9% in overall sensitivity, and 9.2% in overall specificity compared with previous methods. And the lesions can be localized without the labeling data of lesion location in OCT images. Conclusion: The results prove that our method significantly improves the performance and reduces the computation complexity in artificial intelligence assisted analysis of ophthalmic disease through OCT images. Significance: Our method has a significance boost in ophthalmic disease classification and location via convolution transformer. This is applicable to assist ophthalmologists greatly. 1