Anomaly detection via gating highway connection for retinal fundus images

Wentian Zhang, Haozhe Liu, Jinheng Xie, Yawen Huang, Yu Zhang, Yuexiang Li, Raghavendra Ramachandra, Yefeng Zheng

Abstract: Highlights•A gating highway connection module is proposed, which can be applied in a reconstruction-based anomaly detection method to exploit and constrain the intrinsic features during training for better anomaly detection performance.•We integrate histograms of oriented gradients (HOG) prediction as an auxiliary task in the reconstruction network to improve its sensitivity to abnormal images.•Adversarial learning is combined in our multi-task encoder–decoder network in the training phase to improve generalization ability for anomaly detection.•We evaluate our model on two publicly available datasets. The experimental results on image-level and pixel-level tasks prove the effectiveness of gating highway connections, HOG prediction, and adversarial learning for anomaly detection.