Keatmanee Chadaporn, Songsaeng Dittapong, Klabwong Songphon, Nakaguro Yoichi, Kunapinun Alisa, Ekpanyapong Mongkol, Dailey Matthew N
Department of Computer Science, Faculty of Science, Ramkhamhaeng University, Bangkok, Thailand.
Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand.
Quant Imaging Med Surg. 2025 Sep 1;15(9):8579-8593. doi: 10.21037/qims-24-2431. Epub 2025 Aug 18.
The accurate assessment of thyroid nodules, which are increasingly common with age and lifestyle factors, is essential for early malignancy detection. Ultrasound imaging, the primary diagnostic tool for this purpose, holds promise when paired with deep learning. However, challenges persist with small datasets, where conventional data augmentation can introduce noise and obscure essential diagnostic features. To address dataset imbalance and enhance model generalization, this study integrates curriculum learning with a weakly supervised attention network to improve diagnostic accuracy for thyroid nodule classification.
This study integrates curriculum learning with attention-guided data augmentation to improve deep learning model performance in classifying thyroid nodules. Using verified datasets from Siriraj Hospital, the model was trained progressively, beginning with simpler images and gradually incorporating more complex cases. This structured learning approach is designed to enhance the model's diagnostic accuracy by refining its ability to distinguish benign from malignant nodules.
Among the curriculum learning schemes tested, schematic IV achieved the best results, with a precision of 100% for benign and 70% for malignant nodules, a recall of 82% for benign and 100% for malignant, and F1-scores of 90% and 83%, respectively. This structured approach improved the model's diagnostic sensitivity and robustness.
These findings suggest that automated thyroid nodule assessment, supported by curriculum learning, has the potential to complement radiologists in clinical practice, enhancing diagnostic accuracy and aiding in more reliable malignancy detection.
甲状腺结节随着年龄增长和生活方式因素愈发常见，对其进行准确评估对于早期恶性肿瘤检测至关重要。超声成像作为用于此目的的主要诊断工具，与深度学习结合时具有前景。然而，对于小数据集，挑战依然存在，传统的数据增强可能会引入噪声并模糊重要的诊断特征。为了解决数据集不平衡问题并提高模型泛化能力，本研究将课程学习与弱监督注意力网络相结合，以提高甲状腺结节分类的诊断准确性。
本研究将课程学习与注意力引导的数据增强相结合，以提高深度学习模型对甲状腺结节进行分类的性能。使用来自诗里拉吉医院的经过验证的数据集，模型从更简单的图像开始逐步训练，然后逐渐纳入更复杂的病例。这种结构化学习方法旨在通过提升模型区分良性和恶性结节的能力来提高其诊断准确性。
在所测试的课程学习方案中，方案四取得了最佳结果，良性结节的精度为100%，恶性结节的精度为70%，良性结节的召回率为82%，恶性结节的召回率为100%，F1分数分别为90%和83%。这种结构化方法提高了模型的诊断敏感性和稳健性。
这些发现表明，在课程学习支持下的自动化甲状腺结节评估有潜力在临床实践中辅助放射科医生，提高诊断准确性并有助于更可靠地检测恶性肿瘤。
