Detecting emphysema with multiple instance learning
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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. p. 510-513.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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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