计算机科学
特征(语言学)
分割
编码(集合论)
补语(音乐)
图形
代表(政治)
钥匙(锁)
模式识别(心理学)
人工智能
骨龄
理论计算机科学
医学
内科学
表型
生物化学
哲学
基因
政治
集合(抽象数据类型)
化学
互补
程序设计语言
法学
语言学
计算机安全
政治学
作者
Zhichao Yang,Cong Cong,Maurice Pagnucco,Yang Song
标识
DOI:10.1016/j.neunet.2022.11.002
摘要
Bone age assessment plays a significant role in estimating bone maturity. However, radiograph/X-ray images of hand bones contain a large amount of redundant information. Some detection or segmentation based methods have recently been proposed to solve this issue. These network structures are often of high complexity and might require extra annotations, which make them less applicable in practice. In this paper, we present a Multi-scale Multi-reception Attention Net (MMANet), which combines a novel Multi-scale Multi-reception Complement Attention (MMCA) network and a graph attention module with a ResNet backbone to enhance the feature representation of key regions and suppress the influence of background regions to achieve significant performance improvement. Experimental results show our MMANet is able to accurately detect key regions and achieves 3.88 mean absolute error (MAE) on the RSNA 2017 Paediatric Bone Age Challenge dataset. Our method, without explicit modelling of anatomical information, outperforms the current state-of-the-art method (MAE=3.91) by 0.03 (months) which requires extra annotations. Code is available at https://github.com/yzc1122333/BoneAgeAss.
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