Learning to quantify emphysema extent: What labels do we need?

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Standard

Learning to quantify emphysema extent : What labels do we need? / Orting, Silas Nyboe; Petersen, Jens; Thomsen, Laura Hohwu; Winkler Wille, Mathilde Marie; Bruijne, Marleen de.

I: IEEE Journal of Biomedical and Health Informatics, Bind 24, Nr. 4, 2020, s. 1149 -1159.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Orting, SN, Petersen, J, Thomsen, LH, Winkler Wille, MM & Bruijne, MD 2020, 'Learning to quantify emphysema extent: What labels do we need?', IEEE Journal of Biomedical and Health Informatics, bind 24, nr. 4, s. 1149 -1159. https://doi.org/10.1109/JBHI.2019.2932145

APA

Orting, S. N., Petersen, J., Thomsen, L. H., Winkler Wille, M. M., & Bruijne, M. D. (2020). Learning to quantify emphysema extent: What labels do we need? IEEE Journal of Biomedical and Health Informatics, 24(4), 1149 -1159. https://doi.org/10.1109/JBHI.2019.2932145

Vancouver

Orting SN, Petersen J, Thomsen LH, Winkler Wille MM, Bruijne MD. Learning to quantify emphysema extent: What labels do we need? IEEE Journal of Biomedical and Health Informatics. 2020;24(4):1149 -1159. https://doi.org/10.1109/JBHI.2019.2932145

Author

Orting, Silas Nyboe ; Petersen, Jens ; Thomsen, Laura Hohwu ; Winkler Wille, Mathilde Marie ; Bruijne, Marleen de. / Learning to quantify emphysema extent : What labels do we need?. I: IEEE Journal of Biomedical and Health Informatics. 2020 ; Bind 24, Nr. 4. s. 1149 -1159.

Bibtex

@article{7201e6ca6fc24f76920397e39737110e,
title = "Learning to quantify emphysema extent: What labels do we need?",
abstract = "Accurate assessment of pulmonary emphysema is crucial to assess disease severity and subtype, to monitor disease progression, and to predict lung cancer risk. However, visual assessment is time-consuming and subject to substantial inter-rater variability while standard densitometry approaches to quantify emphysema remain inferior to visual scoring. We explore if machine learning methods that learn from a large dataset of visually assessed CT scans can provide accurate estimates of emphysema extent and if methods that learn from emphysema extent scoring can outperform algorithms that learn only from emphysema presence scoring. Four Multiple Instance Learning classifiers, trained on emphysema presence labels, and five Learning with Label Proportions classifiers, trained on emphysema extent labels, are compared. Performance is evaluated on 600 low-dose CT scans from the Danish Lung Cancer Screening Trial and we find that learning from emphysema presence labels, which are much easier to obtain, gives equally good performance to learning from emphysema extent labels. The best performing Multiple Instance Learning and Learning with Label Proportions classifiers, achieve intra-class correlation coefficients around 0.90 and average overall agreement with raters of 78{\%} and 79{\%} compared to an inter-rater agreement of 83.",
author = "Orting, {Silas Nyboe} and Jens Petersen and Thomsen, {Laura Hohwu} and {Winkler Wille}, {Mathilde Marie} and Bruijne, {Marleen de}",
year = "2020",
doi = "10.1109/JBHI.2019.2932145",
language = "English",
volume = "24",
pages = "1149 --1159",
journal = "I E E E Journal of Biomedical and Health Informatics",
issn = "2168-2194",
publisher = "Institute of Electrical and Electronics Engineers",
number = "4",

}

RIS

TY - JOUR

T1 - Learning to quantify emphysema extent

T2 - What labels do we need?

AU - Orting, Silas Nyboe

AU - Petersen, Jens

AU - Thomsen, Laura Hohwu

AU - Winkler Wille, Mathilde Marie

AU - Bruijne, Marleen de

PY - 2020

Y1 - 2020

N2 - Accurate assessment of pulmonary emphysema is crucial to assess disease severity and subtype, to monitor disease progression, and to predict lung cancer risk. However, visual assessment is time-consuming and subject to substantial inter-rater variability while standard densitometry approaches to quantify emphysema remain inferior to visual scoring. We explore if machine learning methods that learn from a large dataset of visually assessed CT scans can provide accurate estimates of emphysema extent and if methods that learn from emphysema extent scoring can outperform algorithms that learn only from emphysema presence scoring. Four Multiple Instance Learning classifiers, trained on emphysema presence labels, and five Learning with Label Proportions classifiers, trained on emphysema extent labels, are compared. Performance is evaluated on 600 low-dose CT scans from the Danish Lung Cancer Screening Trial and we find that learning from emphysema presence labels, which are much easier to obtain, gives equally good performance to learning from emphysema extent labels. The best performing Multiple Instance Learning and Learning with Label Proportions classifiers, achieve intra-class correlation coefficients around 0.90 and average overall agreement with raters of 78% and 79% compared to an inter-rater agreement of 83.

AB - Accurate assessment of pulmonary emphysema is crucial to assess disease severity and subtype, to monitor disease progression, and to predict lung cancer risk. However, visual assessment is time-consuming and subject to substantial inter-rater variability while standard densitometry approaches to quantify emphysema remain inferior to visual scoring. We explore if machine learning methods that learn from a large dataset of visually assessed CT scans can provide accurate estimates of emphysema extent and if methods that learn from emphysema extent scoring can outperform algorithms that learn only from emphysema presence scoring. Four Multiple Instance Learning classifiers, trained on emphysema presence labels, and five Learning with Label Proportions classifiers, trained on emphysema extent labels, are compared. Performance is evaluated on 600 low-dose CT scans from the Danish Lung Cancer Screening Trial and we find that learning from emphysema presence labels, which are much easier to obtain, gives equally good performance to learning from emphysema extent labels. The best performing Multiple Instance Learning and Learning with Label Proportions classifiers, achieve intra-class correlation coefficients around 0.90 and average overall agreement with raters of 78% and 79% compared to an inter-rater agreement of 83.

U2 - 10.1109/JBHI.2019.2932145

DO - 10.1109/JBHI.2019.2932145

M3 - Journal article

C2 - 31380775

VL - 24

SP - 1149

EP - 1159

JO - I E E E Journal of Biomedical and Health Informatics

JF - I E E E Journal of Biomedical and Health Informatics

SN - 2168-2194

IS - 4

ER -

ID: 227843136