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首页> 外文期刊>Engineering Applications of Artificial Intelligence >Integrate domain knowledge in training multi-task cascade deep learning model for benign-malignant thyroid nodule classification on ultrasound images
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Integrate domain knowledge in training multi-task cascade deep learning model for benign-malignant thyroid nodule classification on ultrasound images

机译：集成域名知识在培训多任务级联深度学习模型中对超声图像的良性恶性甲状腺结节分类

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摘要

The automatic and accurate diagnosis of thyroid nodules in ultrasound images is of great significance to reduce the workload and radiologists' misdiagnosis rate. Although deep learning has shown strong image classification performance, the inherent limitations of medical images small dataset and time-consuming access to lesion annotations, leaving this work still facing challenges. In our study, a multi-task cascade deep learning model (MCDLM) was proposed, which integrates radiologists' various domain knowledge (DK) and uses multimodal ultrasound images for automatic diagnosis of thyroid nodules. Specifically, we transfer the knowledge learned by U-net from the source domain to the target domain under the guidance of radiologist' marks to obtain more accurate nodules' segmentation results. We then quantify the nodules' ultrasound features (UF) as conditions to assist the dual-path semi-supervised conditional generative adversarial network (DScGAN) in generating higher quality images obtaining more powerful discriminators. After that, we concatenate DScGAN learning's image representation to train a supervised support vector machine (S3VM) for thyroid nodule classification. The experiment results on ultrasound images of 1030 patients suggest that the MCDLM model can achieve almost the same classification performance as the fully supervised learning (an accuracy of 90.01% and an AUC of 91.07%) using only about 35% of the full labeled dataset, which saves a lot of time and effort compared to traditional methods.
机译：超声图像中的甲状腺结节的自动和准确诊断具有重要意义，以降低工作量和放射学家的误诊率。虽然深度学习表现出强烈的图像分类性能，但医学图像的固有局限性小数据集和对病变注释的耗时的访问，留下这项工作仍面临挑战。在我们的研究中，提出了一种多任务级联深度学习模型（MCDLM），该模型（MCDLM）集成了放射科学家的各种域知识（DK），并使用多模式超声图像进行甲状腺结节的自动诊断。具体地，我们在放射科标记的指导下将U-Net从源域中学习的知识转移到目标域，以获得更准确的结节分段结果。然后，我们将结节的超声特征（UF）量化为协助双路径半监控条件生成的对冲网络（DSCGAN）在获得更高质量的图像获得更强大的鉴别器时。之后，我们连接DSCGAN学习的图像表示，以训练用于甲状腺结节分类的监督支持向量机（S3VM）。在1030名患者的超声图像上的实验结果表明，MCDLM模型可以通过仅35％的完整标签数据集进行全面监督学习（精度为90.01％和91.07％的准确度）。与传统方法相比，它节省了大量的时间和精力。

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