AttenNet: Deep Attention Based Retinal Disease Classification in OCT Images

Abstract

An optical coherence tomography (OCT) image is becoming the standard imaging modality in diagnosing retinal diseases and the assessment of their progression. However, the manual evaluation of the volumetric scan is time consuming, expensive and the signs of the early disease are easy to miss. In this paper, we mainly present an attention-based deep learning method for the retinal disease classification in OCT images, which can assist the large-scale screening or the diagnosis recommendation for an ophthalmologist. First, according to the unique characteristic of a retinal OCT image, we design a customized pre-processing method to improve image quality. Second, in order to guide the network optimization more effectively, a specially designed attention model, which pays more attention to critical regions containing pathological anomalies, is integrated into a typical deep learning network. We evaluate our proposed method on two data sets, and the results consistently show that it outperforms the state-of-the-art methods. We report an overall four-class accuracy of 97.4%, a two-class sensitivity of 100.0%, and a two-class specificity of 100.0% on a public data set shared by Zhang et al. with 1,000 testing B-scans in four disease classes. Compared to their work, our method improves the numbers by 0.8%, 2.2%, and 2.6% respectively.