Pulmonary nodule detection based on Hierarchical-Split HRNet and feature pyramid network with atrous convolution

Accurate pulmonary nodule detection is crucial to the diagnosis of lung diseases. In this work, we propose an end-to-end network for pulmonary nodule detection mainly consisting of pre-processing, detection modules for candidate prediction, and a discriminator to identify the existence of nodules. In the detection module, HS-HRNet is proposed to fulfill feature extraction on high-resolution input for pulmonary nodules that occupy tiny spaces of CT images. HS-HRNet incorporates plug-and-play Hierarchical-Split block into High-Resolution Network (HRNet) and modifies STEM with sandglass module. The main advantages of these modifications are that HS-HRNet can largely promote feature representation ability by split and concatenation operation with no significant increase in computation. In addition, a novel Feature Pyramid Network with Atrous Convolution (AC-FPN) is proposed for multi-scale feature fusion and multi-level prediction. This design allows context and spatial information in different feature levels to be leveraged and extracted under larger receptive fields. Besides, a discriminator replaces false positive reduction modules in most pulmonary detection methods. The discriminator judges the existence of nodules and back-propagates prediction proposals to detection modules through adversarial training. Experiments on publicly available datasets demonstrate competitive performance in pulmonary nodule detection.