Abstract：
Thyroid nodule evaluation using ultrasound is dependent on radiologist experience, but deep learning (DL) models can improve intra-reader agreements. DL model development for medical imaging with small datasets can be challenging. Transfer learning is a technique used in the development of DL models to improve model performance in data-limited scenarios. Here, we investigate the impact of transfer learning with domain-specific RadImageNet dataset and non-medical ImageNet on the robustness of classifying thyroid nodules into benign and malignant. We retrospectively collected 822 ultrasound images of thyroid nodules of patients who underwent fine needle aspiration in our institute. We split our data and used 101 cases in a test set and 721 cases for cross-validation. A Resnet-18 model was trained to classify thyroid nodules into benign and malignant. Then, we trained the same model architecture with transferred weights from ImageNet and RadImageNet. The model without transfer learning for thyroid nodule classification achieved an AUROC of 0.69. The AUROC of our model after transfer learning with ImageNet pre-trained weights was 0.79. Our model achieved an AUROC of 0.83 from transfer learning of the RadImageNet pre-trained weights. The AUROC from the classification model without transfer learning significantly improved after transfer learning with ImageNet (p-value = 0.03) and RadImageNet transfer learning (p-value <0.01). There was a statistically significant distinction in performance between the model utilizing RadImageNet transfer learning and that employing ImageNet transfer learning (p-value <0.01). We demonstrate the potential of RadImageNet as a domain-specific source for transfer learning in thyroid nodule classification.
摘要：
使用超声评估甲状腺结节取决于放射科医生的经验，但深度学习(DL)模型可以改善读者内部协议。使用小数据集进行医学成像的DL模型开发可能具有挑战性。迁移学习是DL模型开发中使用的一种技术，用于在数据有限的场景中提高模型性能。这里,我们研究了使用特定领域的RadImageNet数据集和非医学ImageNet进行迁移学习对将甲状腺结节分为良性和恶性的稳健性的影响.我们回顾性收集了在我们研究所接受细针抽吸的甲状腺结节患者的822张超声图像。我们拆分了我们的数据，并在测试集中使用了101个案例和721个案例进行交叉验证。训练Resnet-18模型以将甲状腺结节分为良性和恶性。然后，我们使用来自ImageNet和RadImageNet的转移权重训练了相同的模型架构。无迁移学习的甲状腺结节分类模型的AUROC为0.69。我们的模型在使用ImageNet预训练权重进行迁移学习后的AUROC为0.79。我们的模型从RadImageNet预训练权重的迁移学习中获得了0.83的AUROC。在使用ImageNet(p值=0.03)和RadImageNet迁移学习(p值<0.01)进行迁移学习之后,来自没有迁移学习的分类模型的AUROC显著改善。使用RadImageNet迁移学习的模型和使用ImageNet迁移学习的模型在性能上存在统计学上的显著差异(p值<0.01)。我们证明了RadImageNet作为甲状腺结节分类中迁移学习的特定领域来源的潜力。
