Fusing Unsupervised and Supervised Deep Learning for White Matter Lesion Segmentation

Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab
Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, PMLR 102:63-72, 2019.

Abstract

Unsupervised Deep Learning for Medical Image Analysis is increasingly gaining attention, since it relieves from the need for annotating training data. Recently, deep generative models and representation learning have lead to new, exciting ways for unsupervised detection and delineation of biomarkers in medical images, such as lesions in brain MR. Yet, Supervised Deep Learning methods usually still perform better in these tasks, due to an optimization for explicit objectives. We aim to combine the advantages of both worlds into a novel framework for learning from both labeled & unlabeled data, and validate our method on the challenging task of White Matter lesion segmentation in brain MR images. The proposed framework relies on modeling normality with deep representation learning for Unsupervised Anomaly Detection, which in turn provides optimization targets for training a supervised segmentation model from unlabeled data. In our experiments we successfully use the method in a Semi-supervised setting for tackling domain shift, a well known problem in MR image analysis, showing dramatically improved generalization. Additionally, our experiments reveal that in a completely Unsupervised setting, the proposed pipeline even outperforms the Deep Learning driven anomaly detection that provides the optimization targets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v102-baur19a, title = {Fusing Unsupervised and Supervised Deep Learning for White Matter Lesion Segmentation}, author = {Baur, Christoph and Wiestler, Benedikt and Albarqouni, Shadi and Navab, Nassir}, booktitle = {Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning}, pages = {63--72}, year = {2019}, editor = {Cardoso, M. Jorge and Feragen, Aasa and Glocker, Ben and Konukoglu, Ender and Oguz, Ipek and Unal, Gozde and Vercauteren, Tom}, volume = {102}, series = {Proceedings of Machine Learning Research}, month = {08--10 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v102/baur19a/baur19a.pdf}, url = {https://proceedings.mlr.press/v102/baur19a.html}, abstract = {Unsupervised Deep Learning for Medical Image Analysis is increasingly gaining attention, since it relieves from the need for annotating training data. Recently, deep generative models and representation learning have lead to new, exciting ways for unsupervised detection and delineation of biomarkers in medical images, such as lesions in brain MR. Yet, Supervised Deep Learning methods usually still perform better in these tasks, due to an optimization for explicit objectives. We aim to combine the advantages of both worlds into a novel framework for learning from both labeled & unlabeled data, and validate our method on the challenging task of White Matter lesion segmentation in brain MR images. The proposed framework relies on modeling normality with deep representation learning for Unsupervised Anomaly Detection, which in turn provides optimization targets for training a supervised segmentation model from unlabeled data. In our experiments we successfully use the method in a Semi-supervised setting for tackling domain shift, a well known problem in MR image analysis, showing dramatically improved generalization. Additionally, our experiments reveal that in a completely Unsupervised setting, the proposed pipeline even outperforms the Deep Learning driven anomaly detection that provides the optimization targets.} }
Endnote
%0 Conference Paper %T Fusing Unsupervised and Supervised Deep Learning for White Matter Lesion Segmentation %A Christoph Baur %A Benedikt Wiestler %A Shadi Albarqouni %A Nassir Navab %B Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2019 %E M. Jorge Cardoso %E Aasa Feragen %E Ben Glocker %E Ender Konukoglu %E Ipek Oguz %E Gozde Unal %E Tom Vercauteren %F pmlr-v102-baur19a %I PMLR %P 63--72 %U https://proceedings.mlr.press/v102/baur19a.html %V 102 %X Unsupervised Deep Learning for Medical Image Analysis is increasingly gaining attention, since it relieves from the need for annotating training data. Recently, deep generative models and representation learning have lead to new, exciting ways for unsupervised detection and delineation of biomarkers in medical images, such as lesions in brain MR. Yet, Supervised Deep Learning methods usually still perform better in these tasks, due to an optimization for explicit objectives. We aim to combine the advantages of both worlds into a novel framework for learning from both labeled & unlabeled data, and validate our method on the challenging task of White Matter lesion segmentation in brain MR images. The proposed framework relies on modeling normality with deep representation learning for Unsupervised Anomaly Detection, which in turn provides optimization targets for training a supervised segmentation model from unlabeled data. In our experiments we successfully use the method in a Semi-supervised setting for tackling domain shift, a well known problem in MR image analysis, showing dramatically improved generalization. Additionally, our experiments reveal that in a completely Unsupervised setting, the proposed pipeline even outperforms the Deep Learning driven anomaly detection that provides the optimization targets.
APA
Baur, C., Wiestler, B., Albarqouni, S. & Navab, N.. (2019). Fusing Unsupervised and Supervised Deep Learning for White Matter Lesion Segmentation. Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 102:63-72 Available from https://proceedings.mlr.press/v102/baur19a.html.

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