Detecting emphysema with multiple instance learning

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

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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. s. 510-513.

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Orting, SN, Petersen, J, Thomsen, LH, Wille, MMW & De Bruijne, M 2018, Detecting emphysema with multiple instance learning. i 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE, s. 510-513, 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Washington, USA, 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. I 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018 (s. 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. I 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE. 2018. s. 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. s. 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 = "5",
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