Global Correlation and Local Geometric Information Coupled Channel Contrast Learning for Thyroid Nodule Risk Stratification

Thyroid nodule risk stratification based on ultra-sound images is vital for follow-up clinical treatment. Due to the complexity of the inter-risk stratification difference, experienced physicians are required to comprehensively analyze all ultrasound signs of the thyroid nodule to diagnose the corresponding risk stratification the thyroid nodule should belong to, however, which is often labor-intensive, subjective, and unstable. To this end, we propose a global correlation and local geometric information coupled channel contrast learning network for thyroid nodule risk stratification based on ultrasound images. Specifically, a channel contrast learning module by combining the contrastive learning with a novel cross-class interaction strategy is proposed to obtain the discriminative feature for different risk stratification levels. Furthermore, multiple ultrasound signs may be observed simultaneously in the same risk stratification level. To introduce the correlation among different ultrasound signs into the feature, a global correlation learning module is proposed by the spectral decomposition of the channel correlation matrix. Additionally, some ultrasound signs used as the basis for judging risk stratification are local characteristics. Therefore, a local geometry learning module is proposed using gradient matrix to model the local information of ultrasound signs to further strengthen the feature. Extensive experiments and comprehensive evaluations confirm that the proposed method achieves superior performance and presents to be promising in thyroid nodule risk stratification.