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Segmentation-based multi-scale attention model for KRAS mutation prediction in rectal cancer

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Abstract

Kirsten Ras (KRAS) mutation identification has great clinical significance to formulate the rectal cancer treatment scheme. Recently, the development of deep learning does much help to improve the computer-aided diagnosis technology. However, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we proposed a joint network named segmentation-based multi-scale attention model (SMSAM) to predict the mutation status of KRAS gene in rectal cancer. More specifically, the network performs segmentation and prediction tasks at the same time. The two tasks mutually transfer knowledge between each other by sharing the same encoder. Meanwhile, two universal multi-scale attention blocks are introduced to ensure that the network more focuses on the region of interest. Besides, we also proposed an entropy branch to provide more discriminative features for the model. Finally, the method is evaluated on internal and external datasets. The results show that the comprehensive performance of SMSAM is better than the existing methods. The code and model have been publicly available.

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Data availability statement

The data that support the findings of this study are available from Department of Radiology of Shanxi Province Cancer Hospital, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Department of Radiology of Shanxi Province Cancer Hospital.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant numbers 61972274); Natural Science Foundation of ShanXi (Grant numbers 201801D121139).

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Authors and Affiliations

  1. College of Information and Computer, Taiyuan University of Technology, Taiyuan, China

    Kai Song, Zijuan Zhao, Jiawen Wang, Yan Qiang, Juanjuan Zhao & Muhammad Bilal Zia

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  1. Kai Song
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  2. Zijuan Zhao
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  3. Jiawen Wang
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Correspondence to Juanjuan Zhao.

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Song, K., Zhao, Z., Wang, J. et al. Segmentation-based multi-scale attention model for KRAS mutation prediction in rectal cancer. Int. J. Mach. Learn. & Cyber. 13, 1283–1299 (2022). https://doi.org/10.1007/s13042-021-01447-w

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