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Residual Attention based Network for Hand Bone Age Assessment
[article]
2018
arXiv
pre-print
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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. ...
Computerized automatic methods have been employed to boost the productivity as well as objectiveness of hand bone age assessment. ...
Residual attention network for bone age assessment: We then use the residual attention network for bone age assessment. ...
arXiv:1901.05876v1
fatcat:zwqk7k367bge7pubrst6ybuvw4
Intelligent Bone Age Assessment: An Automated System to Detect a Bone Growth Problem Using Convolutional Neural Networks with Attention Mechanism
2021
Diagnostics
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Hence, an automated bone age assessment system, which is referred to as Attention-Xception Network (AXNet) is proposed to automatically predict the bone age accurately. ...
The last module will then predict the bone age through the Attention-Xception network that incorporates multiple layers of spatial-attention mechanism to emphasize the important features for more accurate ...
The hand region has been segmented using the Mask R-CNN, while the bone age estimation is performed through a simple residual attention network. ...
doi:10.3390/diagnostics11050765
pmid:33923215
fatcat:tp2umrj3uzbrzmqzc6mkejrrsi
Automatic Radiographic Bone Age Assessment Using Deep Joint Learning with Attention Modules
[chapter]
2020
Advances in Transdisciplinary Engineering
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Hand and wrist skeletal radiographs serve as an important medium for diversified medical and forensic tasks involving bone age assessment. ...
In this work, we present a multi-scale attention-enhanced classifier with a convolutional neural network backbone, specifically designed for bone age prediction and trained to learn a subject's bone age ...
In this paper, we propose a multi-scale residual neural network (termed TJ-Net in this paper) for radiograph-based bone age prediction, introducing the attention mechanism to highlight the features important ...
doi:10.3233/atde200080
fatcat:wfzgbva6zrgpvg2ig7evenwd7a
Automatic Bone Age Assessment of Adolescents Based on Weakly-Supervised Deep Convolutional Neural Networks
2021
IEEE Access
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In this study, a deep convolutional neural network (CNN) model based on fine-grained image classification is proposed, using a hand bone image dataset provided by the Radiological Society of North America ...
of a complete image for bone age classification. ...
ACKNOWLEDGMENT The authors would like to thank the RSNA for providing the dataset. ...
doi:10.1109/access.2021.3108219
fatcat:sca7nsybrvhtvhvapz43qd5uyu
Ridge Regression Neural Network for Pediatric Bone Age Assessment
[article]
2021
arXiv
pre-print
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Experimental evaluation on a dataset of hand radiographs demonstrates the competitive performance of our approach in comparison with existing deep learning based methods for bone age assessment. ...
Bone age is an important measure for assessing the skeletal and biological maturity of children. ...
[16] , an instance segmentation model with residual attention network [18] , regression/classification models [17] , and U-Net based model [22] . ...
arXiv:2104.07785v1
fatcat:ggdz3xcax5a3hd5muox6d5iwd4
Classification of hand‐wrist maturity level based on similarity matching
2021
IET Image Processing
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Judging the maturity level of each hand-wrist reference bone is the core issue in bone age assessment. ...
Relying on the superiority of convolutional neural networks in feature representation, deep learning is widely studied for the automatic bone age assessment. ...
[28] segmented 14 specific bones for bone age assessment from the whole hand-wrist, and then trained an AlexNet convolutional neural network model for each bone's maturity assessment. ...
doi:10.1049/ipr2.12273
fatcat:2qqniw3cznadpkvlduhg76pxsu
Latest Update in Bone Age Assessment Model with Deep Learning
2020
The Bangkok Medical Journal
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In recent years, several deep learning approaches for Bone Age Assessment (BAA) have been purposed. ...
Model architectures Convolutional Neural Network (CNN) and the problem formulation An x-ray image of the hand and wrist are used to determine the discrepancy between skeletal bone age and chronological ...
Latest Update in Bone Age Assessment Model with Deep Learning Automatic Whole-body Bone Age Assessment System suggested by Nguyen et al. 9 employed a modified version of VGGNet 10 by adding Residual ...
doi:10.31524/bkkmedj.2020.23.001
fatcat:4mkz2afxxzfjbn45jej3pvweoe
A Global-Local Feature Fusion Convolutional Neural Network for Bone Age Assessment of Hand X-ray Images
2022
Applied Sciences
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Bone age assessment plays a critical role in the investigation of endocrine, genetic, and growth disorders in children. ...
Compared with other state-of-the-art methods, the proposed global-local network reduces the mean absolute error of the estimated ages to 0.427 years for males and 0.455 years for females; the average accuracy ...
Han [9] automatically extracted key features of the bone age of the left joint based on the residual network (ResNet) model [30] , and automatically assessed the bone age using a convolutional neural ...
doi:10.3390/app12147218
fatcat:rw4omio5i5gnxi6rainyezz3gq
Fully Automatic Model Based on SE-ResNet for Bone Age Assessment
2021
IEEE Access
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INDEX TERMS bone age assessment, deep learning, convolutional neural network, residual network, regression. ...
Bone age assessment (BAA) based on hand X-ray imaging is a common clinical practice for investigating disorders and predicting the adult height of a child. ...
, UNets for segmentation [12, 13] , deep residual network (ResNet)-based models [14] , and CNNs with attention mechanisms [15] . ...
doi:10.1109/access.2021.3074713
fatcat:hzyk6o4gpzgcrjr7ozgkbwjvga
Automated Bone Age Assessment with Image Registration Using Hand X-ray Images
2020
Applied Sciences
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One of the methods for identifying growth disorder is by assessing the skeletal bone age. A child with a healthy growth rate will have approximately the same chronological and bone ages. ...
Recently, the most popular approach in assessing the discrepancy between bone and chronological ages is through the subjective protocol of Tanner–Whitehouse that assesses selected regions in the hand X-ray ...
[36] in order to segment the hand from an X-ray image, where a residual attention network is then used to predict the bone age. ...
doi:10.3390/app10207233
fatcat:6gcb7cyrszhldkrqkefncaqi2m
SMANet: multi-region ensemble of convolutional neural network model for skeletal maturity assessment
2021
Quantitative Imaging in Medicine and Surgery
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±0.13 years (bone age) for the carpal bones-series and 29.9±0.21 points and 0.43±0.17 years, respectively, for the radius, ulna, and short (RUS) bones series based on the Tanner-Whitehouse 3 (TW3) method ...
Bone age assessment (BAA) is a crucial research topic in pediatric radiology. Interest in the development of automated methods for BAA is increasing. ...
Tanner-Whitehouse 3 bone age assessment system; SIMBA, specific identity markers for bone age assessment; SMANet, skeletal maturity assessment network. ...
doi:10.21037/qims-21-1158
pmid:35782257
pmcid:PMC9246748
fatcat:nar3wlgx7vep7h5zpiuktp6tca
Identifying Skeletal Maturity from X-rays using Deep Neural Networks
2021
Open Biomedical Engineering Journal
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Based on 12,611 hand X-Ray images of RSNA Bone Age database, Inception-ResNet-V2 and Xception models have achieved R-Squared value of 0.935 and 0.942 respectively. ...
This paper proposes a comparative analysis between two deep neural network architectures, with the base models such as Inception-ResNet-V2 and Xception-pre-trained networks. ...
Wu et al. [28] incorporated two subnets in their deep learning based pipeline on the RSNA dataset: MASK R-CNN for eliminating background noise and a residual attention subnet based on the aforementioned ...
doi:10.2174/1874120702115010141
fatcat:nelwyhnewrdunkeqzccfmwpjgu
A Cascade Model with Prior Knowledge for Bone Age Assessment
2022
Applied Sciences
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Nevertheless, the existing automated bone age assessment (BAA) models do not consider the nonlinearity and continuity of hand bone development simultaneously. ...
Overall, the model design adequately considers hand bone development features and has high accuracy and consistency, and it also has some applicability on public datasets, showing potential for practical ...
[22] extracted 17 RoIs of the hand using Residual Network (ResNet), and the extracted RoIs were input into the Spatial Transformer Network (STN) for bone age assessment. ...
doi:10.3390/app12157371
fatcat:5sgltcfhjrhpzkcf4mldyqvwja
PRSNet: Part Relation and Selection Network for Bone Age Assessment
[article]
2019
arXiv
pre-print
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In the clinical practice, bone age assessment (BAA) of X-ray images requires the joint consideration of the appearance and location information of hand bones. ...
Bone age is one of the most important indicators for assessing bone's maturity, which can help to interpret human's growth development level and potential progress. ...
Acknowledgments We thank the anonymous reviewers for their constructive comments. ...
arXiv:1909.05651v1
fatcat:w2vqngztcrbzndic34qqipi3tq
Improve bone age assessment by learning from anatomical local regions
[article]
2020
arXiv
pre-print
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Following the spirit of TW2, we propose a novel model called Anatomical Local-Aware Network (ALA-Net) for automatic bone age assessment. ...
Skeletal bone age assessment (BAA), as an essential imaging examination, aims at evaluating the biological and structural maturation of human bones. ...
Related work Earlier deep learning based methods for bone age assessment adopt the end-toend deep neural models, which take the whole hand image as input and make prediction for bone age. Larson et al ...
arXiv:2005.13452v1
fatcat:hvtvjui4unekzlnqs3sspwzbua
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