近年来，甲状腺癌的发病率急剧上升，然而早期超声诊断可降低发病率和死亡率。临床工作严重依赖超声医生的主观经验。虽然存在许多计算机辅助诊断技术，但大多数技术只考虑结果的好坏，而忽略了图像采集前和临床实践中的有用性。为解决这些问题，本研究提出了一种基于注意机制的计算机辅助诊断方法。由于其轻量级属性，该模型可以快速识别结节，并在不使用过多硬件的情况下区分良性和恶性结节。该模型使用边界框定位甲状腺结节，并确定其是否良性或癌症，并输出甲状腺结节超声图像的诊断结果。最新的关注机制可以以较低的成本获得更好的结果。此外，根据甲状腺成像报告和数据系统标准，采集了具有良性和恶性甲状腺结节不同特征的超声图像。实验结果表明，该方法可以快速有效地识别和分类甲状腺结节；结果的mAP值达到0.89，恶性结节的mAP值达到0.94，单幅图像的检测率达到7毫秒。年轻医生和资源有限的小型医院可以受益于使用该方法辅助甲状腺超声检查诊断。 版权所有©2022 Elsevier Ltd。

Incidents of thyroid cancer have dramatically increased in recent years; however, early ultrasound diagnosis can reduce morbidity and mortality. The work in clinical situations relies heavily on the subjective experience of the sonographer. Numerous computer-aided diagnostic techniques exist, but most consider how good the results are, ignoring the pre-image collecting and its usefulness in post-clinical practise. To address these issues, this study proposes a computer-aided diagnosis method based on an attentional mechanism. Due to its lightweight properties, the model can rapidly identify nodules and distinguish between benign and malignant ones without using much hardware. The model uses a bounding box to locate the thyroid nodule and determines whether it is benign or cancerous, and outputs the diagnostic result of the thyroid nodule ultrasound images. The latest attention mechanisms are used to get better results at a fraction of the cost. Additionally, ultrasound images with different features of benign and malignant thyroid nodules were collected following the Thyroid Imaging Reporting and Data System standards. The experimental results showed that the approach identifies and classifies thyroid nodules rapidly and effectively; the mAP value of the results reached 0.89, and the mAP value of malignant nodules reached 0.94, with detection rate of single image reached 7 ms. Young physicians and small hospitals with limited resources can benefit from using this method to assist with thyroid ultrasound examination diagnosis.Copyright © 2022 Elsevier Ltd. All rights reserved.