Domain Anchored Features for Classification of OCT Images

Optical coherence tomography is a crucial imaging technique for the detection and analysis of retinal diseases. Precise classification of optical coherence tomography images helps ophthalmologists and healthcare providers design personalized treatment plans in clinical practice. In this paper, we focus on optical coherence tomography image classification for seven types of retinal diseases and normal retina. Although existing deep neural networks could be applied to optical coherence tomography images for the classification, the features were extracted within same hyperspace, causing "feature congestion". Moreover, the class of normal retina was regarded as a "type of retinal disease", impeding the extraction of true imaging structures for retinal diseases. To deal with the two issues, we innovate a deep neural network module to enhance imaging features so that the enhanced features are more distinct for classification. Consistent to medical findings, we propose two domains of retinal diseases and anchor imaging features onto cross-domains. We tested and evaluated our model on two datasets for eight-classes and four-classes classification, respectively. Our experimental results demonstrated that the proposed module outperforms state-of-the-art methods. We also conducted ablation studies and sensitivity tests for comprehensive evaluation of our method.