Automatical Pulmonary Nodule Detection by Feature Contrast Learning

With regard to pulmonary nodule detection, due to the similar texture and shape as particular tissues, it is difficult for Computer-Aided Detection (CAD) system in detecting pulmonary nodule with both high accuracy and sensitivity. To address this problem, we design a 3D automated pulmonary nodule detection where a auxiliary 3D generative adversarial network is embedded. This well-trained auxiliary component that fully learns volumetrically contextual information of nodule and non-nodule structure, is exploited for each input sample of detection model to generate a derivative which only preserve background context by removing all the nodules. By learning the feature contrast between each input and its derivative, our detection model achieves competitive performance to state-of-the-art approaches for the pulmonary nodule detection task.