Liu Ming, Yao Jianing, Yang Jianli, Wan Zhenzhen, Lin Xiong
Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, People's Republic of China.
College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
Biomed Phys Eng Express. 2024 Dec 26;11(1). doi: 10.1088/2057-1976/ad9f68.
Malignant thyroid nodules are closely linked to cancer, making the precise classification of thyroid nodules into benign and malignant categories highly significant. However, the subtle differences in contour between benign and malignant thyroid nodules, combined with the texture features obscured by the inherent noise in ultrasound images, often result in low classification accuracy in most models. To address this, we propose a Bidirectional Interaction Directional Variance Attention Model based on Increased-Transformer, named IFormer-DVNet. This paper proposes the Increased-Transformer, which enables global feature modeling of feature maps extracted by the Convolutional Feature Extraction Module (CFEM). This design maximally alleviates noise interference in ultrasound images. The Bidirectional Interaction Directional Variance Attention module (BIDVA) dynamically calculates attention weights using the variance of input tensors along both vertical and horizontal directions. This allows the model to focus more effectively on regions with rich information in the image. The vertical and horizontal features are interactively combined to enhance the model's representational capability. During the model training process, we designed a Multi-Dimensional Loss function (MD Loss) to stretch the boundary distance between different classes and reduce the distance between samples of the same class. Additionally, the MD Loss function helps mitigate issues related to class imbalance in the dataset. We evaluated our network model using the public TNCD dataset and a private dataset. The results show that our network achieved an accuracy of 76.55% on the TNCD dataset and 93.02% on the private dataset. Compared to other state-of-the-art classification networks, our model outperformed them across all evaluation metrics.
恶性甲状腺结节与癌症密切相关，因此将甲状腺结节精确分类为良性和恶性类别具有重要意义。然而，良性和恶性甲状腺结节在轮廓上的细微差异，加上超声图像中固有噪声掩盖的纹理特征，导致大多数模型的分类准确率较低。为了解决这个问题，我们提出了一种基于增强型Transformer的双向交互方向方差注意力模型，名为IFormer-DVNet。本文提出了增强型Transformer，它能够对卷积特征提取模块（CFEM）提取的特征图进行全局特征建模。这种设计最大程度地减轻了超声图像中的噪声干扰。双向交互方向方差注意力模块（BIDVA）使用输入张量沿垂直和水平方向的方差动态计算注意力权重。这使得模型能够更有效地聚焦于图像中信息丰富的区域。垂直和水平特征进行交互式组合，以增强模型的表征能力。在模型训练过程中，我们设计了一个多维损失函数（MD Loss）来扩大不同类之间的边界距离，并缩小同一类样本之间的距离。此外，MD损失函数有助于缓解数据集中类不平衡的问题。我们使用公共TNCD数据集和一个私有数据集对我们的网络模型进行了评估。结果表明，我们的网络在TNCD数据集上的准确率为76.55%，在私有数据集上的准确率为93.02%。与其他最先进的分类网络相比，我们的模型在所有评估指标上均优于它们。
