Standard
Deep Learning from Label Proportions for Emphysema Quantification. / Bortsova, Gerda; Dubost, Florian; Ørting, Silas; Katramados, Ioannis; Hogeweg, Laurens; Thomsen, Laura; Wille, Mathilde; de Bruijne, Marleen.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, 2018, Proceedings Part II. ed. / Alejandro F. Frangi; Julia A. Schnabel; Christos Davatzikos; Carlos Alberola-López; Gabor Fichtinger. Springer, 2018. p. 768-776 (Lecture notes in computer science, Vol. 11071).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Bortsova, G, Dubost, F, Ørting, S, Katramados, I, Hogeweg, L, Thomsen, L, Wille, M
& de Bruijne, M 2018,
Deep Learning from Label Proportions for Emphysema Quantification. in AF Frangi, JA Schnabel, C Davatzikos, C Alberola-López & G Fichtinger (eds),
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, 2018, Proceedings Part II. Springer, Lecture notes in computer science, vol. 11071, pp. 768-776, 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, Spain,
16/09/2018.
https://doi.org/10.1007/978-3-030-00934-2_85
APA
Bortsova, G., Dubost, F., Ørting, S., Katramados, I., Hogeweg, L., Thomsen, L., Wille, M.
, & de Bruijne, M. (2018).
Deep Learning from Label Proportions for Emphysema Quantification. In A. F. Frangi, J. A. Schnabel, C. Davatzikos, C. Alberola-López, & G. Fichtinger (Eds.),
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, 2018, Proceedings Part II (pp. 768-776). Springer. Lecture notes in computer science Vol. 11071
https://doi.org/10.1007/978-3-030-00934-2_85
Vancouver
Bortsova G, Dubost F, Ørting S, Katramados I, Hogeweg L, Thomsen L et al.
Deep Learning from Label Proportions for Emphysema Quantification. In Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, 2018, Proceedings Part II. Springer. 2018. p. 768-776. (Lecture notes in computer science, Vol. 11071).
https://doi.org/10.1007/978-3-030-00934-2_85
Author
Bortsova, Gerda ; Dubost, Florian ; Ørting, Silas ; Katramados, Ioannis ; Hogeweg, Laurens ; Thomsen, Laura ; Wille, Mathilde ; de Bruijne, Marleen. / Deep Learning from Label Proportions for Emphysema Quantification. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, 2018, Proceedings Part II. editor / Alejandro F. Frangi ; Julia A. Schnabel ; Christos Davatzikos ; Carlos Alberola-López ; Gabor Fichtinger. Springer, 2018. pp. 768-776 (Lecture notes in computer science, Vol. 11071).
Bibtex
@inproceedings{9e4e047aadf046c7b4390f20251e4d5f,
title = "Deep Learning from Label Proportions for Emphysema Quantification",
abstract = "We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue. These proportions were visually estimated by experts using a standard grading system, in which grades correspond to intervals (label example: 1–5% of diseased tissue). The proposed architecture encodes the knowledge that the labels represent a volumetric proportion. A custom loss is designed to learn with intervals. Thus, during training, our network learns to segment the diseased tissue such that its proportions fit the ground truth intervals. Our architecture and loss combined improve the performance substantially (8% ICC) compared to a more conventional regression network. We outperform traditional lung densitometry and two recently published methods for emphysema quantification by a large margin (at least 7% AUC and 15% ICC), and achieve near-human-level performance. Moreover, our method generates emphysema segmentations that predict the spatial distribution of emphysema at human level.",
keywords = "Emphysema quantification, Learning from label proportions, Multiple instance learning, Weak labels",
author = "Gerda Bortsova and Florian Dubost and Silas {\O}rting and Ioannis Katramados and Laurens Hogeweg and Laura Thomsen and Mathilde Wille and {de Bruijne}, Marleen",
year = "2018",
doi = "10.1007/978-3-030-00934-2_85",
language = "English",
isbn = "9783030009335",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "768--776",
editor = "Frangi, {Alejandro F.} and Schnabel, {Julia A. } and Davatzikos, {Christos } and Alberola-L{\'o}pez, {Carlos } and Fichtinger, {Gabor }",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2018",
address = "Switzerland",
note = "21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 ; Conference date: 16-09-2018 Through 20-09-2018",
}
RIS
TY - GEN
T1 - Deep Learning from Label Proportions for Emphysema Quantification
AU - Bortsova, Gerda
AU - Dubost, Florian
AU - Ørting, Silas
AU - Katramados, Ioannis
AU - Hogeweg, Laurens
AU - Thomsen, Laura
AU - Wille, Mathilde
AU - de Bruijne, Marleen
PY - 2018
Y1 - 2018
N2 - We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue. These proportions were visually estimated by experts using a standard grading system, in which grades correspond to intervals (label example: 1–5% of diseased tissue). The proposed architecture encodes the knowledge that the labels represent a volumetric proportion. A custom loss is designed to learn with intervals. Thus, during training, our network learns to segment the diseased tissue such that its proportions fit the ground truth intervals. Our architecture and loss combined improve the performance substantially (8% ICC) compared to a more conventional regression network. We outperform traditional lung densitometry and two recently published methods for emphysema quantification by a large margin (at least 7% AUC and 15% ICC), and achieve near-human-level performance. Moreover, our method generates emphysema segmentations that predict the spatial distribution of emphysema at human level.
AB - We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue. These proportions were visually estimated by experts using a standard grading system, in which grades correspond to intervals (label example: 1–5% of diseased tissue). The proposed architecture encodes the knowledge that the labels represent a volumetric proportion. A custom loss is designed to learn with intervals. Thus, during training, our network learns to segment the diseased tissue such that its proportions fit the ground truth intervals. Our architecture and loss combined improve the performance substantially (8% ICC) compared to a more conventional regression network. We outperform traditional lung densitometry and two recently published methods for emphysema quantification by a large margin (at least 7% AUC and 15% ICC), and achieve near-human-level performance. Moreover, our method generates emphysema segmentations that predict the spatial distribution of emphysema at human level.
KW - Emphysema quantification
KW - Learning from label proportions
KW - Multiple instance learning
KW - Weak labels
UR - http://www.scopus.com/inward/record.url?scp=85054065475&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00934-2_85
DO - 10.1007/978-3-030-00934-2_85
M3 - Article in proceedings
AN - SCOPUS:85054065475
SN - 9783030009335
T3 - Lecture notes in computer science
SP - 768
EP - 776
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
A2 - Frangi, Alejandro F.
A2 - Schnabel, Julia A.
A2 - Davatzikos, Christos
A2 - Alberola-López, Carlos
A2 - Fichtinger, Gabor
PB - Springer
T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -