A unified representation network for segmentation with missing modalities

Kenneth Lau; Jonas Adler; Jens Sjölund

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arXiv · 1908.06683

A unified representation network for segmentation with missing modalities

Kenneth Lau·Jonas Adler·Jens Sjölund

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Abstract

Over the last few years machine learning has demonstrated groundbreaking results in many areas of medical image analysis, including segmentation. A key assumption, however, is that the train- and test distributions match. We study a realistic scenario where this assumption is clearly violated, namely segmentation with missing input modalities. We describe two neural network approaches that can handle a variable number of input modalities. The first is modality dropout: a simple but surprisingly effective modification of the training. The second is the unified representation network: a network architecture that maps a variable number of input modalities into a unified representation that can be used for downstream tasks such as segmentation. We demonstrate that modality dropout makes a standard segmentation network reasonably robust to missing modalities, but that the same network works even better if trained on the unified representation.

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BibTeX RIS

Kenneth Lau, Jonas Adler, Jens Sjölund. 2019-08-19. A unified representation network for segmentation with missing modalities. https://arxiv.org/abs/1908.06683

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