DCNAS-Net: deformation convolution and neural architecture search detection network for bone marrow oedema

[Multi-sequence relation attention network for diagnosing HLA-B27-negative axial spondyloarthritis]

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.