Deep Learning from Label Proportions for Emphysema Quantification

Gerda Bortsova; Florian Dubost; Silas Ørting; Ioannis Katramados; Laurens Hogeweg; Laura Thomsen; Mathilde Wille; Marleen de Bruijne

doi:10.1007/978-3-030-00934-2_85

Deep Learning from Label Proportions for Emphysema Quantification

Gerda Bortsova^*, Florian Dubost, Silas Ørting, Ioannis Katramados, Laurens Hogeweg, Laura Thomsen, Mathilde Wille, Marleen de Bruijne

^*Corresponding author for this work

5 Citations (Scopus)

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.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 : 21st International Conference, 2018, Proceedings Part II
Editors	Alejandro F. Frangi, Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger
Publisher	Springer
Publication date	2018
Pages	768-776
ISBN (Print)	9783030009335
DOIs	https://doi.org/10.1007/978-3-030-00934-2_85
Publication status	Published - 2018
Event	21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 16 Sept 2018 → 20 Sept 2018

Conference

Conference	21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Country/Territory	Spain
City	Granada
Period	16/09/2018 → 20/09/2018

Series	Lecture notes in computer science
Volume	11071
ISSN	0302-9743

Keywords

Emphysema quantification
Learning from label proportions
Multiple instance learning
Weak labels

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-00934-2_85

https://arxiv.org/pdf/1807.08601.pdfLicence: Other

Cite this

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. https://doi.org/10.1007/978-3-030-00934-2_85

Deep Learning from Label Proportions for Emphysema Quantification. / Bortsova, Gerda; Dubost, Florian; Ørting, Silas et al.
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

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

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). doi: 10.1007/978-3-030-00934-2_85

Bortsova, Gerda ; Dubost, Florian ; Ørting, Silas et al. / 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).

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Deep Learning from Label Proportions for Emphysema Quantification

Abstract

Conference

Keywords

UN SDGs

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