Liver fibrosis MR images classification based on higher-order interaction and sample distribution rebalancing

Ling Zhang; Zhennan Xiao; Wenchao Jiang; Chengbin Luo; Ming Ye; Guanghui Yue; Zhiyuan Chen; Shuman Ouyang; Yupin Liu

doi:10.1007/s13755-023-00255-6

Liver fibrosis MR images classification based on higher-order interaction and sample distribution rebalancing

Health Inf Sci Syst. 2023 Nov 8;11(1):51. doi: 10.1007/s13755-023-00255-6. eCollection 2023 Dec.

Authors

Affiliations

¹ School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China.
² School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060 Guangdong China.
³ Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120 Guangdong China.

^# Contributed equally.

PMID: 37954065
PMCID: PMC10632346 (available on 2024-12-01)
DOI: 10.1007/s13755-023-00255-6

Abstract

The fractal features of liver fibrosis MR images exhibit an irregular fragmented distribution, and the diffuse feature distribution lacks interconnectivity, result- ing in incomplete feature learning and poor recognition accuracy. In this paper, we insert recursive gated convolution into the ResNet18 network to introduce spatial information interactions during the feature learning process and extend it to higher orders using recursion. Higher-order spatial information interactions enhance the correlation between features and enable the neural network to focus more on the pixel-level dependencies, enabling a global interpretation of liver MR images. Additionally, the existence of light scattering and quantum noise during the imaging process, coupled with environmental factors such as breathing artifacts caused by long time breath holding, affects the quality of the MR images. To improve the classification performance of the neural network and better cap- ture sample features, we introduce the Adaptive Rebalance loss function and incorporate the feature paradigm as a learnable adaptive attribute into the angular margin auxiliary function. Adaptive Rebalance loss function can expand the inter-class distance and narrow the intra-class difference to further enhance discriminative ability of the model. We conduct extensive experiments on liver fibrosis MR imaging involving 209 patients. The results demonstrate an average improvement of two percent in recognition accuracy compared to ResNet18. The github is at https://github.com/XZN1233/paper.git.

Keywords: Liver fibrosis; MR imaging; Recursive gated convolution; ResNet18; Sample distribution.

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