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2019 | OriginalPaper | Chapter

Intramodality Domain Adaptation Using Self Ensembling and Adversarial Training

Authors : Zahil Shanis, Samuel Gerber, Mingchen Gao, Andinet Enquobahrie

Published in: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

Publisher: Springer International Publishing

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Abstract

Advances in deep learning techniques have led to compelling achievements in medical image analysis. However, performance of neural network models degrades drastically if the test data is from a domain different from training data. In this paper, we present and evaluate a novel unsupervised domain adaptation (DA) framework for semantic segmentation which uses self ensembling and adversarial training methods to effectively tackle domain shift between MR images. We evaluate our method on two publicly available MRI dataset to address two different types of domain shifts: On the BraTS dataset [11] to mitigate domain shift between high grade and low grade gliomas and on the SCGM dataset [13] to tackle cross institutional domain shift. Through extensive evaluation, we show that our method achieves favorable results on both datasets.

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previous chapter Multi-layer Domain Adaptation for Deep Convolutional Networks

next chapter Learning Interpretable Disentangled Representations Using Adversarial VAEs

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Title: Intramodality Domain Adaptation Using Self Ensembling and Adversarial Training
Authors: Zahil Shanis
Samuel Gerber
Mingchen Gao
Andinet Enquobahrie
Publisher: Springer International Publishing
Book: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data
Print ISBN: 978-3-030-33390-4

Electronic ISBN: 978-3-030-33391-1

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-33391-1_4

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