research-article

Densely-Connected Deep Learning System for Assessment of Skeletal Maturity

Authors:

Yong HuAuthors Info & Claims

SPML '18: Proceedings of the 2018 International Conference on Signal Processing and Machine Learning

Pages 88 - 92

https://doi.org/10.1145/3297067.3297091

Published: 28 November 2018 Publication History

Abstract

Assessment of skeletal maturity plays an essential role in the clinical management of the adolescent disease. This task is very challenging when using machine learning method due to the limited data and large anatomical variations among different subjects. In this paper, we propose a deep learning pipeline to automatically assess the distal radius and ulna (DRU) maturity from left hand radiographs. The model we proposed acquires two convincing advantages: first, our model preserves the maximum information flow and has a much faster convergence rate. Second, our model avoids overfitting even if training with limited data. The proposed method achieves 83.33% and 90.31% for radius and ulna classification respectively.

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Digital Library

Cited By

Wang MBi YHuang HZhang CBao W(2023)Investigating Causal Scope of Radiation Therapy by Uncovering Markov Boundary2023 9th International Conference on Big Data and Information Analytics (BigDIA)10.1109/BigDIA60676.2023.10429614(182-188)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigDIA60676.2023.10429614
Liu LWang HZhu YGe NHu S(2023)Causal Analysis of Bone Disease Risk Factors Based on Markov Boundary Discovery2023 9th International Conference on Big Data and Information Analytics (BigDIA)10.1109/BigDIA60676.2023.10429564(889-896)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigDIA60676.2023.10429564
Wu JZhao YGao JLiu LChen T(2023)Unraveling the Causal Graph: Investigating Disease Etiology through Causal Structure Learning2023 9th International Conference on Big Data and Information Analytics (BigDIA)10.1109/BigDIA60676.2023.10429169(699-706)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigDIA60676.2023.10429169

Index Terms

Densely-Connected Deep Learning System for Assessment of Skeletal Maturity
1. Computing methodologies
  1. Machine learning

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cover image ACM Other conferences

SPML '18: Proceedings of the 2018 International Conference on Signal Processing and Machine Learning

November 2018

177 pages

ISBN:9781450366052

DOI:10.1145/3297067

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 28 November 2018

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National Natural Science Foundations of China

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SPML '18

SPML '18: 2018 International Conference on Signal Processing and Machine Learning

November 28 - 30, 2018

Shanghai, China

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Cited By

Wang MBi YHuang HZhang CBao W(2023)Investigating Causal Scope of Radiation Therapy by Uncovering Markov Boundary2023 9th International Conference on Big Data and Information Analytics (BigDIA)10.1109/BigDIA60676.2023.10429614(182-188)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigDIA60676.2023.10429614
Liu LWang HZhu YGe NHu S(2023)Causal Analysis of Bone Disease Risk Factors Based on Markov Boundary Discovery2023 9th International Conference on Big Data and Information Analytics (BigDIA)10.1109/BigDIA60676.2023.10429564(889-896)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigDIA60676.2023.10429564
Wu JZhao YGao JLiu LChen T(2023)Unraveling the Causal Graph: Investigating Disease Etiology through Causal Structure Learning2023 9th International Conference on Big Data and Information Analytics (BigDIA)10.1109/BigDIA60676.2023.10429169(699-706)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigDIA60676.2023.10429169

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