UASSIGNED:我们的目标是利用深度学习来开发计算机辅助诊断(CAD)系统,以帮助放射科医生在甲状腺超声检查中诊断滤泡性甲状腺癌(FTC)。
UNASSIGNED:1159张图像的数据集,由来自138名FTC患者的351张图像和来自274名良性滤泡型结节患者的808张图像组成,被分为平衡和不平衡的数据集,并用于基于残差网络的迁移学习来训练和测试CAD系统。六位放射科医生参与了实验,以验证所提出的CAD系统是否以及在多大程度上有助于提高其性能。
UNASSIGNED:在平衡数据集上,CAD系统的ROC下面积(AUC)为0.892。准确性,召回,精度,CAD方法的F1评分为84.66%,84.66%,84.77%,84.65%,而初级和高级放射科医生的比例为56.82%,56.82%,56.95%,56.62%和64.20%,64.20%,64.35%,分别为64.11%。在CAD的帮助下,初级和高级放射科医生的指标提高到62.81%,62.81%,62.85%,62.79%和73.86%,73.86%,74.00%,73.83%。结果几乎在不平衡的数据集上重复。结果表明,所提出的CAD方法不仅可以达到比放射科医生更好的性能,而且还显著提高了放射科医师对FTC的诊断。
UNASSIGNED:CAD系统的性能表明它是FTC术前诊断的可靠参考,并可能有助于快速发展,FTC的无障碍筛查方法。
UNASSIGNED: We aim to leverage deep learning to develop a computer aided diagnosis (CAD) system toward helping radiologists in the diagnosis of follicular thyroid carcinoma (FTC) on thyroid ultrasonography.
UNASSIGNED: A dataset of 1159 images, consisting of 351 images from 138 FTC patients and 808 images from 274 benign follicular-pattern nodule patients, was divided into a balanced and unbalanced dataset, and used to train and test the CAD system based on a transfer learning of a residual network. Six radiologists participated in the experiments to verify whether and how much the proposed CAD system helps to improve their performance.
UNASSIGNED: On the balanced dataset, the CAD system achieved 0.892 of area under the ROC (AUC). The accuracy, recall, precision, and F1-score of the CAD method were 84.66%, 84.66%, 84.77%, 84.65%, while those of the junior and senior radiologists were 56.82%, 56.82%, 56.95%, 56.62% and 64.20%, 64.20%, 64.35%, 64.11% respectively. With the help of CAD, the metrics of the junior and senior radiologists improved to 62.81%, 62.81%, 62.85%, 62.79% and 73.86%, 73.86%, 74.00%, 73.83%. The results almost repeated on the unbalanced dataset. The results show the proposed CAD approach can not only achieve better performance than radiologists, but also significantly improve the radiologists\' diagnosis of FTC.
UNASSIGNED: The performances of the CAD system indicate it is a reliable reference for preoperative diagnosis of FTC, and might assist the development of a fast, accessible screening method for FTC.