  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.

Computer Vision and Pattern Recognition;Machine Learning