DP2 Block: An Improved Multi-Scale Block for Pulmonary Nodule Detection

Abstract

Pulmonary nodule detection is a challenging problem in biomedical imaging. Most existing approaches exploit the multi-scale features at a layer level to detect nodule. However, the effect of features at a layer level is limited. This study proposes an improved architecture unit, which we term the 3D DP2 block. Just as its name implies, it is improved by the idea of the 3D dual-path network. It combines multiscale features not only in a layer-wise manner but also at a granular level, which means it can combine global features with local and increases the scales of receptive fields. Moreover, we adopt the coordination-guided convolutional layers (CoordConvs) and design a loss function inspired by the loss of Fast R-CNN. The proposed 3D DP2 block can be easily plugged into the backbone CNN architectures such as the U-Net model without additional parameters introduced while increasing the model accuracy. Our 3D DP2 block based on U-Net is validated on a public LUNA16 dataset. It improves the nodule detection accuracy compared with the baseline model. This demonstrates that pulmonary nodule detection can highly benefit from the multi-scale features at a granular level. And the proposed 3D DP2Net should be useful to other medical detection problems.