Segmentation of Diffuse Lung Abnormality Patterns on Computed Tomography Images using Partially Supervised Learning-Reference-Cited by-同舟云学术

Segmentation of Diffuse Lung Abnormality Patterns on Computed Tomography Images using Partially Supervised Learning

Published:2022 Issue:0 Volume:11 Page:25-36
ISSN:2187-5219
Container-title:Advanced Biomedical Engineering
language:en
Short-container-title:ABE

Author:

Suzuki Yuki¹,Kido Shoji¹,Mabu Shingo²,Yanagawa Masahiro³,Tomiyama Noriyuki³,Sato Yoshinobu⁴

Affiliation:

1. Department of Artificial Intelligence Diagnostic Radiology, Osaka University Graduate School of Medicine

2. Graduate School of Sciences and Technology for Innovation, Yamaguchi University

3. Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine

4. Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology

Publisher

Japanese Society for Medical and Biological Engineering

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Biomedical Engineering,Biomaterials,Biotechnology

Link

https://www.jstage.jst.go.jp/article/abe/11/0/11_11_25/_pdf

Reference37 articles.

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3. 3. Gao M, Bagci U, Lu L, Wu A, Buty M, Shin H-C, Roth H, Papadakis GZ, Depeursinge A, Summers RM, Xu Z, Mollura DJ: Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Comput Methods Biomech Biomed Eng Imaging Vis. 6(1), 1–6, 2018.

4. 4. Bermejo-Peláez D, Ash SY, Washko GR, San José Estépar R, Ledesma-Carbayo MJ: Classification of interstitial lung abnormality patterns with an ensemble of deep convolutional neural networks. Sci Rep. 10(1), 338, 2020.

5. 5. Negahdar M, Coy A, Beymer D: An end-to-end deep learning pipeline for emphysema quantification using multi-label learning. Annu Int Conf IEEE Eng Med Biol Soc. 2019, 929–932, 2019.

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