Accurate diagnosis of thyroid nodules using ultrasonography heavily relies on the superb skills and rich experience of senior radiologists, considering the low contrast, high noise of the ultrasound image, and the diverse appearance of the nodules. Computer-aided diagnosis systems could diagnose thyroid nodules based on ultrasound characteristics to assist radiologists. However, the existing learning-based approaches for detecting and recognizing thyroid nodules have the problems of inaccurate localization and low recognition accuracy. In this study, we propose an Improved Cascade Mask R-CNN for effectively detecting and recognizing thyroid nodules. Firstly, a more effective detector is designed to better classify the ROIs and better correct the bounding boxes. Secondly, a more effective balanced L1 loss function is used to increase the gradient of the easy sample and solve the problem of imbalance between hard samples and easy samples during training. Finally, a more effective soft non-maximum suppression (Soft-NMS) method is used to set an attenuation function for adjacent bounding boxes, which solves the problem of possible missing detection in non-maximum suppression (NMS). The improved model is trained and verified by using real 1408 images collected from the known hospital. Under the localization accuracy of the IoU threshold of 0.5, the mAP reaches 87.1%, and the recognition accuracy reaches 98.67%. The experiment results show that the improved model is effective and highly valuable to help the doctors for the recognition of benign and malignant thyroid nodules.

考虑到超声图像的低对比度，高噪声以及结节的多种多样，使用超声检查对甲状腺结节的准确诊断在很大程度上取决于高级放射科医生的精湛技能和丰富经验。计算机辅助诊断系统可以根据超声特征诊断甲状腺结节，以协助放射科医生。然而，现有的基于学习的用于检测和识别甲状腺结节的方法存在定位不准确和识别精度低的问题。在这项研究中，我们提出了一种改进的级联面罩R-CNN，可以有效地检测和识别甲状腺结节。首先，设计一种更有效的检测器，以更好地对ROI进行分类并更好地校正边界框。第二，更有效的平衡L1损失函数用于增加容易样本的梯度并解决训练过程中硬样本和容易样本之间的不平衡问题。最后，使用一种更有效的软非最大抑制（Soft-NMS）方法来设置相邻边界框的衰减函数，解决了非最大抑制（NMS）中可能丢失检测的问题。通过使用从已知医院收集的真实1408张图像对改进的模型进行训练和验证。在IoU阈值的定位精度为0.5的情况下，mAP达到87.1％，识别精度达到98.67％。实验结果表明，改进后的模型是有效的，对帮助医生识别甲状腺良恶性结节具有很高的价值。