Automatic segmentation of breast cancer histological images based on dual-path feature extraction network

Xi Lu; Xuedong Zhu; Xi Lu; Xuedong Zhu

doi:10.3934/mbe.2022519

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 11: 11137-11153. doi: 10.3934/mbe.2022519

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Research article

Automatic segmentation of breast cancer histological images based on dual-path feature extraction network

Xi Lu ^,,
Xuedong Zhu

School of Mechanical Engineering, Southeast University, Nanjing 211189, China

Academic Editor: Weining Wu

Received: 07 May 2022 Revised: 14 July 2022 Accepted: 18 July 2022 Published: 03 August 2022

The traditional manual breast cancer diagnosis method of pathological images is time-consuming and labor-intensive, and it is easy to be misdiagnosed. Computer-aided diagnosis of WSIs gradually comes into people*s sight. However, the complexity of high-resolution breast cancer pathological images poses a great challenge to automatic diagnosis, and the existing algorithms are often difficult to balance the accuracy and efficiency. In order to solve these problems, this paper proposes an automatic image segmentation method based on dual-path feature extraction network for breast pathological WSIs, which has a good segmentation accuracy. Specifically, inspired by the concept of receptive fields in the human visual system, dilated convolutional networks are introduced to encode rich contextual information. Based on the channel attention mechanism, a feature attention module and a feature fusion module are proposed to effectively filter and combine the features. In addition, this method uses a light-weight backbone network and performs pre-processing on the data, which greatly reduces the computational complexity of the algorithm. Compared with the classic models, it has improved accuracy and efficiency and is highly competitive.
- semantic segmentation,
- breast cancer,
- whole-slide image,
- feature extraction,
- computer-aided diagnosis,
- deep learning
Citation: Xi Lu, Xuedong Zhu. Automatic segmentation of breast cancer histological images based on dual-path feature extraction network[J]. Mathematical Biosciences and Engineering, 2022, 19(11): 11137-11153. doi: 10.3934/mbe.2022519

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Abstract

The traditional manual breast cancer diagnosis method of pathological images is time-consuming and labor-intensive, and it is easy to be misdiagnosed. Computer-aided diagnosis of WSIs gradually comes into people*s sight. However, the complexity of high-resolution breast cancer pathological images poses a great challenge to automatic diagnosis, and the existing algorithms are often difficult to balance the accuracy and efficiency. In order to solve these problems, this paper proposes an automatic image segmentation method based on dual-path feature extraction network for breast pathological WSIs, which has a good segmentation accuracy. Specifically, inspired by the concept of receptive fields in the human visual system, dilated convolutional networks are introduced to encode rich contextual information. Based on the channel attention mechanism, a feature attention module and a feature fusion module are proposed to effectively filter and combine the features. In addition, this method uses a light-weight backbone network and performs pre-processing on the data, which greatly reduces the computational complexity of the algorithm. Compared with the classic models, it has improved accuracy and efficiency and is highly competitive.

References

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