Detecting emphysema with multiple instance learning

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

Standard

Detecting emphysema with multiple instance learning. / Orting, Silas Nyboe; Petersen, Jens; Thomsen, Laura H.; Wille, Mathilde M.W.; De Bruijne, Marleen.

2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE, 2018. p. 510-513.

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

Harvard

Orting, SN, Petersen, J, Thomsen, LH, Wille, MMW & De Bruijne, M 2018, Detecting emphysema with multiple instance learning. in 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE, pp. 510-513, 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Washington, United States, 04/04/2018. https://doi.org/10.1109/ISBI.2018.8363627

APA

Orting, S. N., Petersen, J., Thomsen, L. H., Wille, M. M. W., & De Bruijne, M. (2018). Detecting emphysema with multiple instance learning. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018 (pp. 510-513). IEEE. https://doi.org/10.1109/ISBI.2018.8363627

Vancouver

Orting SN, Petersen J, Thomsen LH, Wille MMW, De Bruijne M. Detecting emphysema with multiple instance learning. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE. 2018. p. 510-513 https://doi.org/10.1109/ISBI.2018.8363627

Author

Orting, Silas Nyboe ; Petersen, Jens ; Thomsen, Laura H. ; Wille, Mathilde M.W. ; De Bruijne, Marleen. / Detecting emphysema with multiple instance learning. 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE, 2018. pp. 510-513

Bibtex

@inproceedings{7f0542b4d6e64f6f919386f94cd21f8b,
title = "Detecting emphysema with multiple instance learning",
abstract = "Emphysema is part of chronic obstructive pulmonary disease, a leading cause of mortality worldwide. Visual assessment of emphysema presence is useful for identifying subjects at risk and for research into disease development. We train a machine learning method to predict emphysema from visually assessed expert labels. We use a multiple instance learning approach to predict both scan-level and region-level emphysema presence. We evaluate performance on 600 low-dose CT scans from the Danish Lung Cancer Screening Study and achieve an AUC of 0.82 for scan-level prediction and AUCs between 0.76 and 0.88 for region-level prediction.",
keywords = "Emphysema, Multiple Instance Learning, Weak supervision",
author = "Orting, {Silas Nyboe} and Jens Petersen and Thomsen, {Laura H.} and Wille, {Mathilde M.W.} and {De Bruijne}, Marleen",
year = "2018",
month = may,
day = "23",
doi = "10.1109/ISBI.2018.8363627",
language = "English",
pages = "510--513",
booktitle = "2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018",
publisher = "IEEE",
note = "15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 ; Conference date: 04-04-2018 Through 07-04-2018",

}

RIS

TY - GEN

T1 - Detecting emphysema with multiple instance learning

AU - Orting, Silas Nyboe

AU - Petersen, Jens

AU - Thomsen, Laura H.

AU - Wille, Mathilde M.W.

AU - De Bruijne, Marleen

PY - 2018/5/23

Y1 - 2018/5/23

N2 - Emphysema is part of chronic obstructive pulmonary disease, a leading cause of mortality worldwide. Visual assessment of emphysema presence is useful for identifying subjects at risk and for research into disease development. We train a machine learning method to predict emphysema from visually assessed expert labels. We use a multiple instance learning approach to predict both scan-level and region-level emphysema presence. We evaluate performance on 600 low-dose CT scans from the Danish Lung Cancer Screening Study and achieve an AUC of 0.82 for scan-level prediction and AUCs between 0.76 and 0.88 for region-level prediction.

AB - Emphysema is part of chronic obstructive pulmonary disease, a leading cause of mortality worldwide. Visual assessment of emphysema presence is useful for identifying subjects at risk and for research into disease development. We train a machine learning method to predict emphysema from visually assessed expert labels. We use a multiple instance learning approach to predict both scan-level and region-level emphysema presence. We evaluate performance on 600 low-dose CT scans from the Danish Lung Cancer Screening Study and achieve an AUC of 0.82 for scan-level prediction and AUCs between 0.76 and 0.88 for region-level prediction.

KW - Emphysema

KW - Multiple Instance Learning

KW - Weak supervision

UR - http://www.scopus.com/inward/record.url?scp=85048089312&partnerID=8YFLogxK

U2 - 10.1109/ISBI.2018.8363627

DO - 10.1109/ISBI.2018.8363627

M3 - Article in proceedings

AN - SCOPUS:85048089312

SP - 510

EP - 513

BT - 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018

PB - IEEE

T2 - 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018

Y2 - 4 April 2018 through 7 April 2018

ER -

ID: 199968017