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[Multi-sequence relation attention network for diagnosing HLA-B27-negative axial spondyloarthritis]
1School of Biomedical Engineering, Southern Medical University//Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou 510515, China.
Objective:
To develop a new 3D multi-sequence relation attention network for exploring the complementary and correlation information of different magnetic resonance imaging (MRI) modalities and improving the diagnostic performance of HLA-B27-negative axial spondyloarthropathy (axSpA).
Methods:
We retrospectively collected T1-weighted imaging (T1WI) and fat suppuration MRI (FS-MRI) data and clinical data of 375 HLA-B27-negative patients from the Third Affiliated Hospital of Southern Medical University (including 164 axSpA and 211 non-axSpA patients) and 49 patients from Nanhai Hospital (including 27 axSpA and 22 non-axSpA patients) between January, 2010 and August, 2021.A 3D relation attention network MSFANet based on multi-sequence MRI was used for automatic diagnosis of axSpA against non-axSpA in these patients.MSFANet consisted of a shallow shared feature learning module and a class-aware feature learning module, and latter module used a 3D multi-sequence relation attention mechanism to refine and fuse multi-sequence MRI features.A hybrid loss function was used to enhance the recognition ability of MSFANet by learning the loss weight coefficients of different branches to improve the classification performance.
Results:
The experimental results demonstrated that MSFANet outperformed several state-of-the-art fusion algorithms (P<0.05) with AUC, accuracy, sensitivity, and specificity of 0.840, 77.93%, 83.70%, and 70.29% in the internal validation set, and of 0.783, 74.47%, 82.43% and 70.40% in the independent external validation set, respectively.The ablation studies showed that under the same architecture, the fusion model was superior to single-sequence models, which confirmed the effectiveness and necessity of fusing multi-sequence MRI.The visualization results demonstrated that MSFANet could focus on learning information from abnormal areas on MRI during the classification.
Conclusion:
We successfully constructed a 3D deep neural network based on multi-sequence MRI for differential diagnosis of HLA-B27-negative axSpA against nonaxSpA and verified the effectiveness of the multisequence relation attention mechanism for promoting classification performance of the network.
