用深度学习自动分割并判断甲状腺结节良恶性，结果更准且可解释。
A Deep Learning Framework for Thyroid Nodule Segmentation and Malignancy Classification from Ultrasound Images
- 两阶段框架：先用TransUNet分割结节，再用ResNet-18分类局部区域。
- 在349张图像上F1-score达0.852，优于传统特征方法的0.829。
- 端到端自动化，适合临床辅助诊断与可解释性研究者使用。
基于超声的甲状腺结节风险分层是关键临床任务，但存在高人之间差异。现有深度学习模型多为“黑箱”，本文提出一种全自动、两阶段可解释恶性预测框架。通过强制模型仅关注临床相关区域实现可解释性：首先使用TransUNet自动分割甲状腺结节，生成掩码后提取结节周围感兴趣区域，再将局部图像输入ResNet-18分类器进行恶性预测。在包含349张图像的临床数据集上采用5折交叉验证，该框架在恶性预测任务中取得0.852的高F1分数。对比使用手工形态特征的随机森林基线模型（F1=0.829），表明从局部结节中学习的隐式视觉特征比显式形状特征更具预测力。这是首个实现超声图像中甲状腺结节检测与恶性度预测的全自动端到端流程。
原文摘要 · Abstract (English)
Ultrasound-based risk stratification of thyroid nodules is a critical clinical task, but it suffers from high inter-observer variability. While many deep learning (DL) models function as "black boxes," we propose a fully automated, two-stage framework for interpretable malignancy prediction. Our method achieves interpretability by forcing the model to focus only on clinically relevant regions. First, a TransUNet model automatically segments the thyroid nodule. The resulting mask is then used to create a region of interest around the nodule, and this localised image is fed directly into a ResNet-18 classifier. We evaluated our framework using 5-fold cross-validation on a clinical dataset of 349 images, where it achieved a high F1-score of 0.852 for predicting malignancy. To validate its performance, we compared it against a strong baseline using a Random Forest classifier with hand-crafted morphological features, which achieved an F1-score of 0.829. The superior performance of our DL framework suggests that the implicit visual features learned from the localised nodule are more predictive than explicit shape features alone. This is the first fully automated end-to-end pipeline for both detecting thyroid nodules on ultrasound images and predicting their malignancy.
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