Abstract
Improving the accuracy of early diagnosis is the key to prolong the survival of lung cancer. Lung Nodule Detection algorithms based on Deep Learning have made significant contributions to improving the accuracy. However, it remains a challenge to reduce the False Positive rate while maintaining high sensitivity. In this paper, we propose a novel MLP-based False Positive Reduction network, Wave-Involution MLP. We design a progressive multi-scale fusion block based on the novel operator Involution to fuse global features preferably. Moreover, inspired by quantum theory, we design a CT-WaveMLP feature extraction backbone, which transforms CT images into wave functions and enhances feature extraction capability. We performed experiments on LUNAV2 dataset, and the results show that our network achieves the average CPM of 0.861, which has a better performance compared with mainstream methods.
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Zhang, Z., Liu, F., Qi, L., Tie, Y. (2023). WINMLP: Quantum & Involution Inspire False Positive Reduction in Lung Nodule Detection. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13625. Springer, Cham. https://doi.org/10.1007/978-3-031-30111-7_6
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