四川大学电气工程学院 2. 四川大学华西第二医院
纸质出版:2022
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王俊峰, 程勃超, 刘彦, 等. 多尺度特征双线性融合的骨龄自动评估方法[J]. 传感器与微系统, 2022,(5):48-52.
多尺度特征双线性融合的骨龄自动评估方法[J]. 2022, (5): 48-52.
王俊峰, 程勃超, 刘彦, 等. 多尺度特征双线性融合的骨龄自动评估方法[J]. 传感器与微系统, 2022,(5):48-52. DOI: 10.13873/J.1000-9787(2022)05-0048-05.
多尺度特征双线性融合的骨龄自动评估方法[J]. 2022, (5): 48-52. DOI: 10.13873/J.1000-9787(2022)05-0048-05.
针对传统人工评估骨龄方法受主观因素影响大、专业性要求高这一问题,提出一种多尺度特征双线性融合的骨龄自动评估框架。由感兴趣区域(ROI)分割网络和骨龄回归网络组成。ROI分割网络利用类激活图映射机制在弱监督条件下分割出不同尺度的手骨区域作为回归网络的输入。回归网络以EfficientNet作为特征提取器,通过嵌入双线性池化模块进行多尺度特征融合,增加性别信息以平衡不同性别手骨发育的差异,并利用分层交叉验证来降低数据集划分对实验结果的影响。最后在临床数据集和RSNA骨龄挑战数据集上评估模型,预测骨龄的平均绝对值误差分别为4.86个月和5.77个月。实验结果表明:嵌入双线性池化模块融合多尺度特征能有效提升骨龄评估精确度,可应用于临床辅助评估。
针对传统人工评估骨龄方法受主观因素影响大、专业性要求高这一问题,提出一种多尺度特征双线性融合的骨龄自动评估框架。由感兴趣区域(ROI)分割网络和骨龄回归网络组成。ROI分割网络利用类激活图映射机制在弱监督条件下分割出不同尺度的手骨区域作为回归网络的输入。回归网络以EfficientNet作为特征提取器,通过嵌入双线性池化模块进行多尺度特征融合,增加性别信息以平衡不同性别手骨发育的差异,并利用分层交叉验证来降低数据集划分对实验结果的影响。最后在临床数据集和RSNA骨龄挑战数据集上评估模型,预测骨龄的平均绝对值误差分别为4.86个月和5.77个月。实验结果表明:嵌入双线性池化模块融合多尺度特征能有效提升骨龄评估精确度,可应用于临床辅助评估。
基于深度学习的手骨X射线图像骨龄评估 [J]. 王嘉庆,梅礼晔,张俊华. 计算机工程 . 2021(01)
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Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding. [J] . Akira Fukui,Dong Huk Park,Daylen Yang,Anna Rohrbach,Trevor Darrell,Marcus Rohrbach. CoRR . 2016
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Bone age estimation and prediction of final height in patients with β-thalassaemia major: a comparison between the two most common methods [J] . Athanasios Christoforidis,Maria Badouraki,George Katzos,Miranda Athanassiou-Metaxa. Pediatric Radiology . 2007 (12)
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