Unsupervised Domain Adaptation for Cross-Device OCT Lesion Detection via Learning Adaptive Features

Optical coherence tomography (OCT) is widely used in computer-aided medical diagnosis of retinal pathologies. Deep convolutional network has been successfully applied to detect lesions from OCT images. Different OCT imaging devices inevitably cause variation in the distribution between training phase and testing phase, which will lead to extremely reduction on model performance. Most existing unsupervised domain adaptation methods are mainly focused on lesion segmentation, there are few studies on lesion detection tasks especially for OCT images. In this paper, we propose a novel unsupervised domain adaptation framework adaptively learning feature representation to achieve cross-device lesion detection for OCT images. Firstly, we design global and local adversarial discriminators to force the networks to learn device-independent features. Secondly, we develop a non-parameter adaptive feature norm into global adversarial discriminator to stabilize the discrimination in target domain. Finally, we perform the validation experiment on lesion detection task across two OCT devices. The results exhibit that the proposed framework has promising performance compared with other unsupervised domain adaptation approaches.