Deep Multi-label Classification in Affine Subspaces

Kurmann, Thomas; Márquez-Neila, Pablo; Wolf, Sebastian; Sznitman, Raphael (2019). Deep Multi-label Classification in Affine Subspaces. In: Shen, Dinggang; Liu, Tianming; Peters, Terry M.; Staib, Lawrence H.; Essert, Caroline; Zhou, Sean; Yap, Pew-Thian; Khan, Ali (eds.) MICCAI 2019. Lecture Notes in Computer Science: Vol. 11764 (pp. 165-173). Cham: Springer International Publishing 10.1007/978-3-030-32239-7_19

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Multi-label classification (MLC) problems are becoming increasingly popular in the context of medical imaging. This has in part been driven by the fact that acquiring annotations for MLC is far less burdensome than for semantic segmentation and yet provides more expressiveness than multi-class classification. However, to train MLCs, most methods have resorted to similar objective functions as with traditional multi-class classification settings. We show in this work that such approaches are not optimal and instead propose a novel deep MLC classification method in affine subspace. At its core, the method attempts to pull features of class-labels towards different affine subspaces while maximizing the distance between them. We evaluate the method using two MLC medical imaging datasets and show a large performance increase compared to previous multi-label frameworks. This method can be seen as a plug-in replacement loss function and is trainable in an end-to-end fashion.

Item Type:

Conference or Workshop Item (Paper)


10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Ophthalmology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Medical Imaging Laboratory

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Kurmann, Thomas Kevin, Márquez Neila, Pablo, Wolf, Sebastian (B), Sznitman, Raphael


500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
000 Computer science, knowledge & systems
600 Technology > 620 Engineering




Lecture Notes in Computer Science


Springer International Publishing




Thomas Kevin Kurmann

Date Deposited:

25 Oct 2019 15:11

Last Modified:

05 Dec 2022 15:31

Publisher DOI:





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