基于专家咨询的多模态特征融合和知识驱动学习的甲状腺结节分类
Multimodal Feature Fusion and Knowledge-Driven Learning via Experts Consult for Thyroid Nodule Classification
摘要：
计算机辅助诊断（CAD）正在成为一种重要的方法，以帮助临床医生跨越多个领域。这些自动化系统利用各种计算机视觉（CV）程序以及人工智能（AI）技术来制定给定图像的诊断，例如，计算机断层扫描和超声波。这两个领域（CV和AI）的进步使CAD系统的性能不断提高，最终可以避免进行细针穿刺等侵入性手术。在这项研究中，一个新的端到端的知识驱动的分类框架。该系统侧重于由甲状腺超声检查生成的多模态数据，并通过将甲状腺结节分类为良性和恶性类别来充当CAD系统。具体来说，所提出的系统利用专家集合提供的线索来指导密集连接卷积网络（DenseNet）的学习阶段。该集成由在ImageNet上预训练的各种网络组成，包括AlexNet，ResNet，VGG等。先前计算的多模态特征参数用于通过转移学习创建超声领域专家，此外，减少了训练所需的样本数量。为了验证所提出的方法，进行了大量的实验，提供了详细的性能专家集成和知识驱动的DenseNet。如结果所示，所提出的系统在甲状腺结节分类任务的定性指标方面实现了相关性能，从而在制定诊断时产生了巨大的资产。
英文摘要：
Computer-aided diagnosis (CAD) is becoming a prominent approach to assist clinicians spanning across multiple fields. These automated systems take advantage of various computer vision (CV) procedures, as well as artificial intelligence (AI) techniques, to formulate a diagnosis of a given image, e.g., computed tomography and ultrasound. Advances in both areas (CV and AI) are enabling ever increasing performances of CAD systems, which can ultimately avoid performing invasive procedures such as fine-needle aspiration. In this study, a novel end-to-end knowledge-driven classification framework is presented. The system focuses on multimodal data generated by thyroid ultrasonography, and acts as a CAD system by providing a thyroid nodule classification into the benign and malignant categories. Specifically, the proposed system leverages cues provided by an ensemble of experts to guide the learning phase of a densely connected convolutional network (DenseNet). The ensemble is composed by various networks pretrained on ImageNet, including AlexNet, ResNet, VGG, and others. The previously computed multimodal feature parameters are used to create ultrasonography domain experts via transfer learning, decreasing, moreover, the number of samples required for training. To validate the proposed method, extensive experiments were performed, providing detailed performances for both the experts ensemble and the knowledge-driven DenseNet. As demonstrated by the results, the proposed system achieves relevant performances in terms of qualitative metrics for the thyroid nodule classification task, thus resulting in a great asset when formulating a diagnosis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
