Optical coherence tomography (OCT) is widely used in the diagnosis of retinal diseases. Reading OCT images and summarizing its insights is a routine, yet nonetheless time-consuming task. Automatic report generation can alleviate this issue. There are two major challenges in this task: (1) An OCT image may contain several fundus abnormalities and it is difficult to detect them all simultaneously. (2) The diagnostic reports are complex, which need to describe multiple lesions. In this paper, we propose a deep learning-based model, named as VSTA model (Visual and Semantic Topic Attention model), which is able to generate report from the input OCT image. Our major contributions include: (1) Semantic attention and visual attention are jointly embedded to the model to generate diagnosis report with complex content. (2) Semantic tags based on image similarity is employed to initialize the semantic attention weights, which increases the prediction accuracy of the model. With the proposed VSTA model, the metric of BLEU-4, CIDEr and ROUGE-L reach 31.16, 264.22 and 52.58, which are better than some existing advanced methods.
Optical coherence tomography (OCT), a non-invasive high-resolution imaging technology of retinal tissues, has been widely used in the diagnosis of retinal diseases. However, the shortage of ophthalmologists and the overloaded work have caused great difficulties in screening for retinal diseases. Therefore, developing an accurate automatic diagnosis system for screening retinal diseases in OCT images is essential for the prevention and treatment of retinal diseases. To this end, we propose a novel multi-view-based automatic aided diagnosis method for simultaneously screening multiple diseases in retinal OCT images. First, we collected 11,211 cases of 11 common retinal diseases from the ophthalmology clinic, and each case included two OCTs acquired from different views. Then, to automatically and accurately screen diseases in retinal OCT images, a novel multi-view attention network is proposed for screening retinal diseases based on the collected data. Finally, we conduct experiments based on the collected clinical data to evaluate the performance of the proposed method. The AUC of the proposed method achieves 0.9023, which indicates the effectiveness of the proposed method.
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