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LCRNet: local cross-channel recalibration network for liver cancer classification based on CT images

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Abstract

Liver cancer is the leading cause of mortality in the world. Over the years, researchers have spent much effort in developing computer-aided techniques to improve clinicians’ diagnosis efficiency and precision, aiming at helping patients with liver cancer to take treatment early. Recently, attention mechanisms can enhance the representational power of convolutional neural networks (CNNs), which have been widely used in medical image analysis. In this paper, we propose a novel architectural unit, local cross-channel recalibration (LCR) module, dynamically adjusting the relative importance of intermediate feature maps by considering the roles of different global context features and building the local dependencies between channels. LCR first extracts different global context features and integrates them by global context integration operator, then estimates per channel attention weight with a local cross-channel interaction manner. We combine the LCR module with the residual block to form a Residual-LCR module and construct a deep neural network termed local cross-channel recalibration network (LCRNet) based on a stack of Residual-LCR modules to recognize live cancer atomically based on CT images. Furthermore, This paper collects a clinical CT image dataset of liver cancer, AMU-CT, to verify the effectiveness of LCRNet, which will be publicly available. The experiments on the AMU-CT dataset and public SD-OCT dataset demonstrate our LCRNet significantly outperforms state-of-the-art attention-based CNNs. Specifically, our LCRNet improves accuracy by over 11% than ECANet on the AMU-CT dataset.

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Data availability

AMU-CT dataset is provided as supplementary material, which is available at https://github.com/TommyLitlle/LCRNet.

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Acknowledgements

This work was supported part by University Natural Science Research Project of Anhui Province (No.KJ2021ZD0021).

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Author notes

  1. Qiang Fang and Yue Yang have contributed equally to this work.

Authors and Affiliations

  1. Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230000, China

    Qiang Fang, Jiangming Chen, Zixiang Chen, Tian Pu & Fubao Liu

  2. Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230000, China

    Yue Yang

  3. Shenzhen Raysight Intelligent Medical Technology Co., Ltd, Shenzhen, 518063, China

    Hao Wang

  4. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

    Hanxi Sun & Xiaoqing Zhang

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  1. Qiang Fang
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Correspondence to Qiang Fang, Xiaoqing Zhang or Fubao Liu.

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Fang, Q., Yang, Y., Wang, H. et al. LCRNet: local cross-channel recalibration network for liver cancer classification based on CT images. Health Inf Sci Syst 12, 5 (2024). https://doi.org/10.1007/s13755-023-00263-6

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