结合课程学习和弱监督的注意力,在超声波成像中加强甲状腺结节的评估
Chadaporn Keatmanee1, Dittapong Songsaeng2, Songphon Klabwong3
1Department of Computer Science, Faculty of Science, Ramkhamhaeng University, Bangkok, Thailand.
Quantitative imaging in medicine and surgery
|September 2, 2025
概括
课程学习增强了甲状腺结节分类的深度学习,提高了区分良性和恶性结节的准确性. 这种人工智能方法有助于放射科医生早期发现癌症.
科学领域:
- 医学成像
- 人工智能
- 癌症学
背景情况:
- 甲状腺结节很常见,需要准确的评估来早期检测恶性瘤.
- 超声波成像与深度学习相结合,
- 传统的数据增强可以掩盖小型数据集的诊断特征.
研究的目的:
- 提高甲状腺结节分类的深度学习模型性能.
- 解决数据集不平衡问题,增强模型通用化.
- 为了更好的诊断准确性, 将课程学习与弱监督的注意网络整合起来.
主要方法:
- 综合课程学习与注意力引导的数据增强.
- 在经过验证的甲状腺结节数据集上逐步训练深度学习模型.
- 使用结构化的学习方法,从简单的图像开始,到复杂的案例.
主要成果:
- 最好的成绩是课程计划IV.
- 在良性结节方面达到100%的精度,在恶性结节方面达到70%.
- 在良性结节方面达到82%的回忆率,恶性结节则达到100%,F1分数分别为90%和83%.
结论:
- 课程学习显著提高了甲状腺结节分类的诊断灵敏度和稳定性.
- 使用课程学习的自动评估可以帮助放射科医生.
- 这种方法提高了诊断的准确性,并有助于可靠地检测恶性瘤.
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