Article

Identification of Incorrect Karyotypes Using Deep Learning

Authors:

Chengchuang Lin,

Shuangyin LiAuthors Info & Claims

Artificial Neural Networks and Machine Learning – ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I

Pages 453 - 464

https://doi.org/10.1007/978-3-030-86362-3_37

Published: 14 September 2021 Publication History

Abstract

Karyotyping is a vital cytogenetics technique widely applied in prenatal diagnosis and genetic screening. Heavily dependent on the experience of the cytogeneticist and easily affected by the attention, karyotype analysis is a time-consuming and error-prone task, and incorrect karyotypes may result in misdiagnosis conclusions. This paper proposes an effective identification framework for incorrect karyotypes based on deep learning technology. Firstly, a chromosome classifier is trained and utilized to classify chromosome instances in karyotypes performed manually by cytogeneticists. Afterward, when the categories of chromosome instances classified by the classifier are not identical to those categories classified by cytogeneticists, the proposed framework identifies these corresponding karyotypes as unreliable. Finally, the expert team review these unreliable karyotypes and confirmed their correctness. Extensive experiments show that the proposed framework achieves 100% recall and 88.89% F1 score on incorrect karyotypes, which demonstrates the advancement and promising effectiveness of the proposed framework to address the issue of incorrect karyotypes.

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Cited By

Xia CWang JQin YGu YChen BYang J(2022)An End-to-End Combinatorial Optimization Method for R-band Chromosome Recognition with Grouping Guided AttentionMedical Image Computing and Computer Assisted Intervention – MICCAI 202210.1007/978-3-031-16440-8_1(3-13)Online publication date: 18-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-16440-8_1

Index Terms

Identification of Incorrect Karyotypes Using Deep Learning
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Guide Proceedings

Artificial Neural Networks and Machine Learning – ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I

Sep 2021

629 pages

ISBN:978-3-030-86361-6

DOI:10.1007/978-3-030-86362-3

Editors:
Igor Farkaš
Comenius University in Bratislava, Bratislava, Slovakia
,
Paolo Masulli
iMotions A/S, Copenhagen, Denmark
,
Sebastian Otte
University of Tübingen, Tübingen, Baden-Württemberg, Germany
,
Stefan Wermter
Universität Hamburg, Hamburg, Germany

© Springer Nature Switzerland AG 2021.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 14 September 2021

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Cited By

Xia CWang JQin YGu YChen BYang J(2022)An End-to-End Combinatorial Optimization Method for R-band Chromosome Recognition with Grouping Guided AttentionMedical Image Computing and Computer Assisted Intervention – MICCAI 202210.1007/978-3-031-16440-8_1(3-13)Online publication date: 18-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-16440-8_1

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