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CPSNet: a cyclic pyramid-based small lesion detection network

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Abstract

The presence of small lesions is an important marker for determining whether a patient will develop malignant tumors. Clinical practitioners could easily overlook the presence of small lesions, meaning automated approaches are essential for screening test results. The use of deep learning-based detectors for this purpose has so far been suboptimal as small lesions easily lose the spatial information during the convolution operation, resulting in unsatisfactory detection accuracy and limited application in clinical decision making. In this paper, we propose a Cyclic Pyramid-based Small lesion detection Network (CPSNet), which iteratively enhances the features in the parallel layer of the Feature Parallel Network (FPN), the features learned in the loop are fused again with the initial FPN to compensate for the inadequacy problem in the initial training. In addition, we propose an aggregated dilation block (ADB) to capture small variations at different scales and a global attention block (GAB) to adaptively recalibrate the channel-based feature responses while focusing on the target spatial information and highlighting the most relevant feature channels. Extensive experiments on eight organs included in the DeepLesion dataset show that our method has a high detection accuracy(mAP=60.4) and a high overall sensitivity(80.5%), which is superior to the state-of-art methods.

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Acknowledgements

The authors would like to thank radiologists of the Medical Imaging Department of Affiliated Hospital of Jiangsu University. This work was supported by the National Natural Science Foundation of China (61976106); China Postdoctoral Science Foundation (2017M611737); Six talent peaks project in Jiangsu Province (DZXX-122); Key RESEARCH and development program for social development (SH2021056); Zhenjiang city key research and development plan (SH2020011); Jiangsu province emergency management science and technology project (YJGL-TG-2020-8).

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Authors and Affiliations

  1. Department of Imaging, Affiliated Hospital of Jiangsu University, Zhenjiang, China

    Yan Zhu

  2. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China

    Zhe Liu, Yuqing Song, Kai Han, Chengjian Qiu, YangYang Tang & Yi Liu

  3. Department of Radiology, Fudan University, Shanghai, China

    Jiawen Zhang

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Correspondence to Yi Liu.

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This paper is the expanding version of conference paper that was published in International Conference on Artificial Intelligence and Security. The corresponding reference is Tang, Y., Liu, Z., Song, Y., Han, K., Su, J., Wang, W., ... & Zhang, J. Automatic CT Lesion Detection Based on Feature Pyramid Inference with Multi-scale Response In International Conference on Artificial Intelligence and Security, pp. 167-179, Springer, Cham, 2021.

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Zhu, Y., Liu, Z., Song, Y. et al. CPSNet: a cyclic pyramid-based small lesion detection network. Multimed Tools Appl 83, 39983–40001 (2024). https://doi.org/10.1007/s11042-023-17024-y

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  • DOI: https://doi.org/10.1007/s11042-023-17024-y

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