Positive-Aware Lesion Detection Network with Cross-scale Feature Pyramid for OCT Images

Abstract

Optical coherence tomography (OCT) is an important imaging technique in ophthalmology, and accurate detection of retinal lesions plays an important role in computer-aided diagnosis. However, the particularities of retinal lesions, such as their complex appearance and large variation of scale, limit the successful application of conventional deep learning-based object detection networks for OCT lesion detection. In this study, we propose a positive-aware lesion detection network with cross-scale feature pyramid for OCT images. A cross-scale boost module with non-local network is firstly applied to enhance the ability of feature representation for OCT lesions with varying scales. To avoid lesion omission and misdetection, some positive-aware network designs are then added into a two-stage detection network, including global level positive estimation and local level positive mining. Finally, we establish a large OCT dataset with multiple retinal lesions, and perform sufficient comparative experiments on it. The results demonstrate that our proposed network achieves 92.36 mean average precision (mAP) for OCT lesion detection, which is superior to other existing detection approaches.