# Deep Multi Label Classification in Affine Subspaces

## TL;DR

This paper introduces a novel deep multi-label classification method in affine subspaces that improves performance on medical imaging datasets by better separating class features.

## Contribution

The paper proposes a new deep MLC approach in affine subspaces that outperforms existing methods and can be integrated as a plug-in loss function.

## Key findings

- Significant performance improvements on medical imaging datasets.
- Effective separation of class-label features in affine subspaces.
- End-to-end trainable with existing architectures.

## Abstract

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.