目的:在临床实践中,甲状腺结节通常由专家医师使用2D超声图像进行视觉评估。根据他们的评估,可以推荐细针抽吸术(FNA)。然而,从超声图像对甲状腺结节进行视觉分类可能会导致患者不必要的细针穿刺。这项研究的目的是开发一种自动甲状腺超声图像分类系统,以防止不必要的FNA。
方法:提出了一种自动计算机辅助人工智能系统,用于使用基于DenseNet架构的微调深度学习模型对甲状腺结节进行分类,其中包含一个注意模块。该数据集包括基于Bethesda评分分类的591张甲状腺结节图像。甲状腺结节被分类为需要FNA或不需要FNA。在此任务中遇到的挑战包括管理图像质量的可变性,解决超声图像数据集中伪影的存在,解决阶级不平衡问题,并确保模型的可解释性。我们采用了数据增强等技术,类权重,和梯度加权类激活图(Grad-CAM),以增强模型性能并提供对决策的见解。
结果:我们的方法取得了出色的结果,平均准确度为0.94,F1评分为0.93,灵敏度为0.96。Grad-CAM的使用提供了有关决策的见解,然后从最终用户的角度加强了二元分类的可靠性。
结论:我们提出了一种深度学习架构,可以有效地从超声图像中将甲状腺结节分类为需要FNA或不需要FNA。尽管与图像可变性相关的挑战,阶级不平衡,和可解释性,我们的方法展示了高分类精度和最小的假阴性,显示其在临床环境中减少不必要的FNA的潜力。
OBJECTIVE: In clinical practice, thyroid nodules are typically visually evaluated by expert physicians using 2D ultrasound images. Based on their assessment, a fine needle aspiration (FNA) may be recommended. However, visually classifying thyroid nodules from ultrasound images may lead to unnecessary fine needle aspirations for patients. The aim of this study is to develop an automatic thyroid ultrasound image classification system to prevent unnecessary FNAs.
METHODS: An automatic computer-aided artificial intelligence system is proposed for classifying thyroid nodules using a fine-tuned deep learning model based on the DenseNet architecture, which incorporates an attention module. The dataset comprises 591 thyroid nodule images categorized based on the Bethesda score. Thyroid nodules are classified as either requiring FNA or not. The challenges encountered in this task include managing variability in image quality, addressing the presence of artifacts in ultrasound image datasets, tackling class imbalance, and ensuring model interpretability. We employed techniques such as data augmentation, class weighting, and gradient-weighted class activation maps (Grad-CAM) to enhance model performance and provide insights into decision making.
RESULTS: Our approach achieved excellent results with an average accuracy of 0.94, F1-score of 0.93, and sensitivity of 0.96. The use of Grad-CAM gives insights on the decision making and then reinforce the reliability of the binary classification for the end-user perspective.
CONCLUSIONS: We propose a deep learning architecture that effectively classifies thyroid nodules as requiring FNA or not from ultrasound images. Despite challenges related to image variability, class imbalance, and interpretability, our method demonstrated a high classification accuracy with minimal false negatives, showing its potential to reduce unnecessary FNAs in clinical settings.