Wang Shuhuan, Zhang Shuangqingyue, Liao Lingmin, Zhang Chunquan, Xu Debin, Huang Long, Ma He
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110169, China.
Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, 1 Minde Road, Nanchang, Jiangxi, 330006, China.
Med Eng Phys. 2025 Feb;136:104288. doi: 10.1016/j.medengphy.2025.104288. Epub 2025 Jan 10.
In this paper, a two-stage task weakly supervised learning algorithm is proposed. It accurately achieved patient-level classification task of benign and malignant thyroid nodules based on ultrasound images from two scanning angles: long axis and short axis of the thyroid site. In the first stage, 68,208 ultrasound scanning images of 588 patients are used to train the underlying classification model. In the second stage, feature vectors of ultrasound images with dual scan angles are extracted using the classification model in the first stage. Then the feature vectors are assigned to position sequences in the order of visual reception by the physician. Finally, the location decision is made through a weakly supervised learning approach. Combined with the dual-angle difference information carried in the overall features, our method accurately achieved benign and malignant classification of thyroid nodules at the patient level. An accuracy of 93.81 % for benign and malignant classification of patients was obtained in our test set. The accuracy of benign and malignant classification of patients with thyroid nodules is improved by our weakly supervised learning method based on a two-stage classification task. It also reduced the pressure of imaging physicians in diagnosing a large number of images. In the clinical auxiliary diagnosis, it provides an effective reference for the timely determination of thyroid nodule patients.
本文提出了一种两阶段任务弱监督学习算法。该算法基于甲状腺部位长轴和短轴两个扫描角度的超声图像，准确地实现了甲状腺结节良恶性的患者级分类任务。在第一阶段，使用588例患者的68208幅超声扫描图像训练基础分类模型。在第二阶段，利用第一阶段的分类模型提取双扫描角度超声图像的特征向量。然后，将特征向量按照医生视觉接收的顺序分配到位置序列中。最后，通过弱监督学习方法进行位置决策。结合整体特征中携带的双角度差异信息，我们的方法准确地实现了患者级甲状腺结节的良恶性分类。在我们的测试集中，患者良恶性分类的准确率达到了93.81%。基于两阶段分类任务的弱监督学习方法提高了甲状腺结节患者良恶性分类的准确率。它还减轻了影像科医生诊断大量图像的压力。在临床辅助诊断中，为及时确定甲状腺结节患者提供了有效的参考。
