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Residual Attention based Network for Hand Bone Age Assessment [article]

Eric Wu, Bin Kong, Xin Wang, Junjie Bai, Yi Lu, Feng Gao, Shaoting Zhang, Kunlin Cao, Qi Song, Siwei Lyu, Youbing Yin
<span title="2018-12-21">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.05876v1">arXiv:1901.05876v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zwqk7k367bge7pubrst6ybuvw4">fatcat:zwqk7k367bge7pubrst6ybuvw4</a> </span>
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Intelligent Bone Age Assessment: An Automated System to Detect a Bone Growth Problem Using Convolutional Neural Networks with Attention Mechanism

Mohd Asyraf Zulkifley, Nur Ayuni Mohamed, Siti Raihanah Abdani, Nor Azwan Mohamed Kamari, Asraf Mohamed Moubark, Ahmad Asrul Ibrahim
<span title="2021-04-24">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/abr6zdbfebblxlzh436eqtiagi" style="color: black;">Diagnostics</a> </i> &nbsp;
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.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/diagnostics11050765">doi:10.3390/diagnostics11050765</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33923215">pmid:33923215</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tp2umrj3uzbrzmqzc6mkejrrsi">fatcat:tp2umrj3uzbrzmqzc6mkejrrsi</a> </span>
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Automatic Bone Age Assessment of Adolescents Based on Weakly-Supervised Deep Convolutional Neural Networks

Kexin Li, Jingzhe Zhang, Yunfei Sun, Xinwang Huang, Chunxue Sun, Qiancheng Xie, Shijie Cong
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
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.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3108219">doi:10.1109/access.2021.3108219</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sca7nsybrvhtvhvapz43qd5uyu">fatcat:sca7nsybrvhtvhvapz43qd5uyu</a> </span>
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Ridge Regression Neural Network for Pediatric Bone Age Assessment [article]

Ibrahim Salim, A. Ben Hamza
<span title="2021-04-15">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2104.07785v1">arXiv:2104.07785v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ggdz3xcax5a3hd5muox6d5iwd4">fatcat:ggdz3xcax5a3hd5muox6d5iwd4</a> </span>
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Classification of hand‐wrist maturity level based on similarity matching

Keji Mao, Lijian Chen, Minhao Wang, Ruiji Xu, Xiaomin Zhao
<span title="2021-06-05">2021</span> <i title="Institution of Engineering and Technology (IET)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dscocsarvbb5boe7eixzypaetu" style="color: black;">IET Image Processing</a> </i> &nbsp;
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.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1049/ipr2.12273">doi:10.1049/ipr2.12273</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2qqniw3cznadpkvlduhg76pxsu">fatcat:2qqniw3cznadpkvlduhg76pxsu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210715065641/https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ipr2.12273" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9d/be/9dbef9ede237eb59cf47e017993b736bfda2ffa3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1049/ipr2.12273"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Latest Update in Bone Age Assessment Model with Deep Learning

Chadaporn Keatmanee, Songphon Klabwong, Chittiwat Suprasongsin, Kamolphong Osatavanichvong, Chirotchana Suchato
<span title="2020-09-26">2020</span> <i title="Bangkok Dusit Medical Service (BDMS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/auwklnb4ojc4rldrc4otauqmny" style="color: black;">The Bangkok Medical Journal</a> </i> &nbsp;
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  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.31524/bkkmedj.2020.23.001">doi:10.31524/bkkmedj.2020.23.001</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4mkz2afxxzfjbn45jej3pvweoe">fatcat:4mkz2afxxzfjbn45jej3pvweoe</a> </span>
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Automated Bone Age Assessment with Image Registration Using Hand X-ray Images

Mohd Asyraf Zulkifley, Siti Raihanah Abdani, Nuraisyah Hani Zulkifley
<span title="2020-10-16">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a> </i> &nbsp;
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.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app10207233">doi:10.3390/app10207233</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6gcb7cyrszhldkrqkefncaqi2m">fatcat:6gcb7cyrszhldkrqkefncaqi2m</a> </span>
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SMANet: multi-region ensemble of convolutional neural network model for skeletal maturity assessment

Yi Zhang, Wenwen Zhu, Kai Li, Dong Yan, Hua Liu, Jie Bai, Fan Liu, Xiaoguang Cheng, Tongning Wu
<span title="">2021</span> <i title="AME Publishing Company"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/loautdcoere2pnt4xwr4hiv3yi" style="color: black;">Quantitative Imaging in Medicine and Surgery</a> </i> &nbsp;
±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.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.21037/qims-21-1158">doi:10.21037/qims-21-1158</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35782257">pmid:35782257</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC9246748/">pmcid:PMC9246748</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nar3wlgx7vep7h5zpiuktp6tca">fatcat:nar3wlgx7vep7h5zpiuktp6tca</a> </span>
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Fully Automatic Model Based on SE-ResNet for Bone Age Assessment

Jin He, Dan Jiang
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
, UNets for segmentation [12, 13] , deep residual network (ResNet)-based models [14] , and CNNs with attention mechanisms [15] .  ...  Figure 1 shows some typical hand radiographs of different ages for BAA. The bone joint and cartilage of children's hands of different ages are obviously different.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3074713">doi:10.1109/access.2021.3074713</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hzyk6o4gpzgcrjr7ozgkbwjvga">fatcat:hzyk6o4gpzgcrjr7ozgkbwjvga</a> </span>
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Identifying Skeletal Maturity from X-rays using Deep Neural Networks

Suprava Patnaik, Sourodip Ghosh, Richik Ghosh, Shreya Sahay
<span title="2021-12-31">2021</span> <i title="Bentham Science Publishers Ltd."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/goskthjdqjh4pdgqtfule25lhy" style="color: black;">Open Biomedical Engineering Journal</a> </i> &nbsp;
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  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2174/1874120702115010141">doi:10.2174/1874120702115010141</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nelwyhnewrdunkeqzccfmwpjgu">fatcat:nelwyhnewrdunkeqzccfmwpjgu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220204022312/https://openbiomedicalengineeringjournal.com/contents/volumes/V15/TOBEJ-15-141/TOBEJ-15-141.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6d/84/6d84c30483a1f4aea588f251c848e5756893fc8c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2174/1874120702115010141"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

PRSNet: Part Relation and Selection Network for Bone Age Assessment [article]

Yuanfeng Ji and Hao Chen and Dan Lin and Xiaohua Wu and Di Lin
<span title="2019-09-05">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.05651v1">arXiv:1909.05651v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/w2vqngztcrbzndic34qqipi3tq">fatcat:w2vqngztcrbzndic34qqipi3tq</a> </span>
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Improve bone age assessment by learning from anatomical local regions [article]

Dong Wang, Kexin Zhang, Jia Ding, Liwei Wang
<span title="2020-05-27">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.13452v1">arXiv:2005.13452v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hvtvjui4unekzlnqs3sspwzbua">fatcat:hvtvjui4unekzlnqs3sspwzbua</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200529041501/https://arxiv.org/pdf/2005.13452v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.13452v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions

Muhammad Waqas Nadeem, Hock Guan Goh, Abid Ali, Muzammil Hussain, Muhammad Adnan Khan, Vasaki a/p Ponnusamy
<span title="2020-10-03">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/abr6zdbfebblxlzh436eqtiagi" style="color: black;">Diagnostics</a> </i> &nbsp;
By considering a wide range of deep-learning applications, the main aim of this paper is to present a detailed survey on emerging research of deep-learning models for bone age assessment (e.g., segmentation  ...  An enormous number of scientific research publications related to bone age assessment using deep learning are explored, studied, and presented in this survey.  ...  Therefore, the segmentation of US-based images is important for bone age assessment.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/diagnostics10100781">doi:10.3390/diagnostics10100781</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33022947">pmid:33022947</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/k2bqi6crzjf3zlbpchehhjkwx4">fatcat:k2bqi6crzjf3zlbpchehhjkwx4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201004020357/https://res.mdpi.com/d_attachment/diagnostics/diagnostics-10-00781/article_deploy/diagnostics-10-00781.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/10/8f/108f00f4d6920c6a2204587d162d723cec6d075c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/diagnostics10100781"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

SIMBA: Specific Identity Markers for Bone Age Assessment [article]

Cristina González and María Escobar and Laura Daza and Felipe Torres and Gustavo Triana and Pablo Arbeláez
<span title="2020-07-13">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
With this lack of available methods as motivation, we present SIMBA: Specific Identity Markers for Bone Age Assessment.  ...  SIMBA is a novel approach for the task of BAA based on the use of identity markers.  ...  Based on this analysis, we can conclude that SIMBA is not biased towards relative age and learns to predict a residual bone age based entirely on the visual input and the guidance of the identity markers  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.05454v2">arXiv:2007.05454v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tyy6uutxsjhh3k5tocosstnz3m">fatcat:tyy6uutxsjhh3k5tocosstnz3m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200721073723/https://arxiv.org/pdf/2007.05454v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.05454v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Using deep learning to analyze the compositeness of musculoskeletal aging reveals that spine, hip and knee age at different rates, and are associated with different genetic and non-genetic factors [article]

Alan Le Goallec, Samuel Diai, Sasha Collin, Theo Vincent, Chirag J Patel
<span title="2021-06-22">2021</span> <i title="Cold Spring Harbor Laboratory"> medRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
joints and bones in the body age at different rates.  ...  Our predictor is composite and can be used to assess spine age, hip age and knee age, in addition to general musculoskeletal aging.  ...  Bone Age: A Handy Tool for Pediatric Providers. Pediatrics 140, (2017). 24. Hao, P. Y. et al. Skeletal bone age assessments for young children based on regression convolutional neural networks. Math.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2021.06.14.21258896">doi:10.1101/2021.06.14.21258896</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ij23kxxeyfh7vc4vq7p7yxpaba">fatcat:ij23kxxeyfh7vc4vq7p7yxpaba</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210715075530/https://www.medrxiv.org/content/medrxiv/early/2021/06/22/2021.06.14.21258896.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/84/d4/84d4edad61c79dc2d5ad13f3d8017a60099d57b8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2021.06.14.21258896"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> medrxiv.org </button> </a>
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