Quantum-Involution inspire false positive reduction in pulmonary nodule detection

Abstract

Lung cancer is a potentially fatal disease worldwide, and improving the accuracy of early lung cancer diagnosis plays a key role in improving the prognosis of patients. In recent years, as Deep Learning (DL) is increasingly applied in the field of Pulmonary Nodule Detection (PND), computer-aided systems for PND based on DL have made significant contributions. However, due to the stringent requirement for high sensitivity, many computer-aided PND systems inevitably have high false positive rate. Reducing the false positive rate continue to face challenge due to the variable morphology of pulmonary nodules. In this study, a novel MultiLayer Perceptron (MLP)-based False Positive Reduction network, Wave-Involution MLP (WINMLP), is proposed to reduce the false positive rate from a new perspective. We design a Progressive Multi-Scale Fusion block based on the novel Involution to fuse global features preferably. Moreover, inspired by Quantum theory, we design a CT-WaveMLP backbone, which transforms CT images into Wave functions and enhances feature extraction capability. We performed experiments on LUNA V2 dataset and the results show WINMLP achieves the average CPM of 0.861, which has a better performance compared with existing excellent methods.