Spectral Data Augmentation Techniques to Quantify Lung Pathology from CT-Images

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Spectral Data Augmentation Techniques to Quantify Lung Pathology from CT-Images. / Kayal, Subhradeep; Dubost, Florian; Tiddens, Harm A.W.M.; De Bruijne, Marleen.

ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging. IEEE, 2020. p. 586-590 9098581.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Kayal, S, Dubost, F, Tiddens, HAWM & De Bruijne, M 2020, Spectral Data Augmentation Techniques to Quantify Lung Pathology from CT-Images. in ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging., 9098581, IEEE, pp. 586-590, 17th IEEE International Symposium on Biomedical Imaging, ISBI 2020, Iowa City, United States, 03/04/2020. https://doi.org/10.1109/ISBI45749.2020.9098581

APA

Kayal, S., Dubost, F., Tiddens, H. A. W. M., & De Bruijne, M. (2020). Spectral Data Augmentation Techniques to Quantify Lung Pathology from CT-Images. In ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging (pp. 586-590). [9098581] IEEE. https://doi.org/10.1109/ISBI45749.2020.9098581

Vancouver

Kayal S, Dubost F, Tiddens HAWM, De Bruijne M. Spectral Data Augmentation Techniques to Quantify Lung Pathology from CT-Images. In ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging. IEEE. 2020. p. 586-590. 9098581 https://doi.org/10.1109/ISBI45749.2020.9098581

Author

Kayal, Subhradeep ; Dubost, Florian ; Tiddens, Harm A.W.M. ; De Bruijne, Marleen. / Spectral Data Augmentation Techniques to Quantify Lung Pathology from CT-Images. ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging. IEEE, 2020. pp. 586-590

Bibtex

@inproceedings{b9862d3d33ee49e1b54b3425eb37bd31,
title = "Spectral Data Augmentation Techniques to Quantify Lung Pathology from CT-Images",
abstract = "Data augmentation is of paramount importance in biomedical image processing tasks, characterized by inadequate amounts of labelled data, to best use all of the data that is present. In-use techniques range from intensity transformations and elastic deformations, to linearly combining existing data points to make new ones. In this work, we propose the use of spectral techniques for data augmentation, using the discrete cosine and wavelet transforms. We empirically evaluate our approaches on a CT texture analysis task to detect abnormal lung-tissue in patients with cystic fibrosis. Empirical experiments show that the proposed spectral methods perform favourably as compared to the existing methods. When used in combination with existing methods, our proposed approach can increase the relative minor class segmentation performance by 44.1% over a simple replication baseline.",
keywords = "Cystic Fibrosis, Data Augmentation, Discrete Cosine Transform, Discrete Wavelet Transform, Lung CT, Lung Texture Analysis, Spectral Transforms",
author = "Subhradeep Kayal and Florian Dubost and Tiddens, {Harm A.W.M.} and {De Bruijne}, Marleen",
year = "2020",
month = apr,
doi = "10.1109/ISBI45749.2020.9098581",
language = "English",
pages = "586--590",
booktitle = "ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging",
publisher = "IEEE",
note = "17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 ; Conference date: 03-04-2020 Through 07-04-2020",

}

RIS

TY - GEN

T1 - Spectral Data Augmentation Techniques to Quantify Lung Pathology from CT-Images

AU - Kayal, Subhradeep

AU - Dubost, Florian

AU - Tiddens, Harm A.W.M.

AU - De Bruijne, Marleen

PY - 2020/4

Y1 - 2020/4

N2 - Data augmentation is of paramount importance in biomedical image processing tasks, characterized by inadequate amounts of labelled data, to best use all of the data that is present. In-use techniques range from intensity transformations and elastic deformations, to linearly combining existing data points to make new ones. In this work, we propose the use of spectral techniques for data augmentation, using the discrete cosine and wavelet transforms. We empirically evaluate our approaches on a CT texture analysis task to detect abnormal lung-tissue in patients with cystic fibrosis. Empirical experiments show that the proposed spectral methods perform favourably as compared to the existing methods. When used in combination with existing methods, our proposed approach can increase the relative minor class segmentation performance by 44.1% over a simple replication baseline.

AB - Data augmentation is of paramount importance in biomedical image processing tasks, characterized by inadequate amounts of labelled data, to best use all of the data that is present. In-use techniques range from intensity transformations and elastic deformations, to linearly combining existing data points to make new ones. In this work, we propose the use of spectral techniques for data augmentation, using the discrete cosine and wavelet transforms. We empirically evaluate our approaches on a CT texture analysis task to detect abnormal lung-tissue in patients with cystic fibrosis. Empirical experiments show that the proposed spectral methods perform favourably as compared to the existing methods. When used in combination with existing methods, our proposed approach can increase the relative minor class segmentation performance by 44.1% over a simple replication baseline.

KW - Cystic Fibrosis

KW - Data Augmentation

KW - Discrete Cosine Transform

KW - Discrete Wavelet Transform

KW - Lung CT

KW - Lung Texture Analysis

KW - Spectral Transforms

U2 - 10.1109/ISBI45749.2020.9098581

DO - 10.1109/ISBI45749.2020.9098581

M3 - Article in proceedings

AN - SCOPUS:85085857028

SP - 586

EP - 590

BT - ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging

PB - IEEE

T2 - 17th IEEE International Symposium on Biomedical Imaging, ISBI 2020

Y2 - 3 April 2020 through 7 April 2020

ER -

ID: 248471614