Residual Attention Based Network for Hand Bone Age Assessment

Bin Kong · Xin Wang · Junjie Bai · Yi Lu · Feng Gao · Shaoting Zhang +4 more

53 citations0/0 files storedIEEE International Symposium on Biomedical Imaging (ISBI)

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Forensic Anthropology and Bioarchaeology Studies Advanced Neural Network Applications Medical Imaging and Analysis

Abstract

Computerized automatic methods have been employed to boost the productivity as well as objectiveness of hand bone age assessment. These approaches make predictions according to the whole X-ray images, which include other objects that may introduce distractions. Instead, our framework is inspired by the clinical workflow (Tanner-Whitehouse) of hand bone age assessment, which focuses on the key components of the hand. The proposed framework is composed of two components: a Mask R-CNN subnet of pixelwise hand segmentation and a residual attention network for hand bone age assessment. The Mask R-CNN subnet segments the hands from X-ray images to avoid the distractions of other objects (e.g., X-ray tags). The hierarchical attention components of the residual attention subnet force our network to focus on the key components of the X-ray images and generate the final predictions as well as the associated visual supports, which is similar to the assessment procedure of clinicians. We evaluate the performance of the proposed pipeline on the RSNA pediatric bone age dataset1and the results demonstrate its superiority over the previous methods.1http://rsnachallenges.cloudapp.net/competitions/4

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References

1959RADIOGRAPHIC ATLAS OF SKELETAL DEVELOPMENT OF THE HAND AND WRIST 2017Fully Automated Deep Learning System for Bone Age Assessment 2009The BoneXpert Method for Automated Determination of Skeletal Maturity 2016Deep learning for automated skeletal bone age assessment in X-ray images 2018Paediatric Bone Age Assessment Using Deep Convolutional Neural Networks 2017Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks

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2021Attention-Guided Discriminative Region Localization and Label Distribution Learning for Bone Age Assessment 2021Fully Automatic Model Based on SE-ResNet for Bone Age Assessment 2021Ridge regression neural network for pediatric bone age assessment 2020Self-Supervised Attention Mechanism for Pediatric Bone Age Assessment With Efficient Weak Annotation 2020Automated Bone Age Assessment with Image Registration Using Hand X-ray Images 2023Bone age assessment based on deep neural networks with annotation-free cascaded critical bone region extraction 2022RAGCN: Region Aggregation Graph Convolutional Network for Bone Age Assessment From X-Ray Images

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