用模糊标签评估骨年龄的多分支注意力学习
Bishi He1, Zhe Xu1, Dong Zhou1
1School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
这项研究引入了一种新的深度学习模型MAAL-Net,用于准确评估东亚儿童的骨年龄. 该模型有效地使用模两可的放射性标签来改善儿科内分泌和代谢疾病的诊断.
科学领域:
- 儿科内分泌学 儿科内分泌学
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
背景情况:
- 骨龄评估 (BAA) 对于诊断儿科内分泌和代谢疾病至关重要.
- 目前在西方数据 (RSNA) 上训练的深度学习BAA模型,由于发展和标准化差异,对东亚人口的准确性有限.
- 获得大型,准确标记的儿科骨龄数据集是具有挑战性和劳动密集的.
研究的目的:
- 开发一个准确的深度学习模型来评估东亚儿童的骨年龄.
- 用西方数据集训练现有模型的局限性.
- 为了提高模型培训效率,利用模两可的放射性标签.
主要方法:
- 收集了来自东亚人口的新型骨年龄数据集 (CNBA).
- 利用放射学报告中的模两可的标签,将其转化为高斯分布标签.
- 建议使用模两可的标签网络 (MAAL-Net) 进行多分支注意力学习,包括手对象定位和基于注意力的特征提取模块.
- 使用图像级标签识别了感兴趣的信息区域 (ROI).
主要成果:
- 在RSNA和CNBA数据集上,MAAL-Net实现了与最先进的方法相比具有竞争力的性能.
- 该模型在儿科骨年龄评估中表现出与经验丰富的医生相当的表现.
- 拟议的方法有效地处理了对强大的BAA的模两可的标签.
结论:
- MAAL-Net提供了一个有前途的解决方案,用于准确有效地评估东亚儿童的骨龄.
- 使用模两可的标签和多部门注意力学习的方法可以克服数据稀缺性并提高模型通用性.
- 这项工作有助于在儿科内分泌学中推进AI驱动的诊断工具.
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