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The scarcity of labeled data often impedes the application of deep learning to the segmentation of medical images.
Deep co-training for semi-supervised image segmentation
Jizong Peng, Guillermo Estradab, Marco Pedersoli, and Christian Desrosiers · 1903
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Unsupervised classifiers, mutual information and’phantom targets
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The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 2000
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Mutual information estimation in higher dimensions: A speed-up of a k-nearest neighbor based estimator
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Representation learning: A review and new perspectives
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Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 2016
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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Mehdi Noroozi and Paolo Favaro · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI
Xiahai Zhuang and Juan Shen · 2016
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Semi-supervised learning for network-based cardiac MR image segmentation
Wenjia Bai, Ozan Oktay, Matthew Sinclair, Hideaki Suzuki, Martin Rajchl, Giacomo Tarroni, Ben Glocker, Andrew King, Paul M Matthews, and Daniel Rueckert · 2017
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Semi-supervised deep learning for fully convolutional networks
Christoph Baur, Shadi Albarqouni, and Nassir Navab · 2017
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Learning discrete representations via information maximizing self-augmented training
Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, and Masashi Sugiyama · 2017
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Semi supervised semantic segmentation using generative adversarial network
Nasim Souly, Concetto Spampinato, and Mubarak Shah · 2017
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Deep adversarial networks for biomedical image segmentation utilizing unannotated images
Yizhe Zhang, Lin Yang, Jianxu Chen, Maridel Fredericksen, David P Hughes, and Danny Z Chen · 2017
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MINE: mutual information neural estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and R Devon Hjelm · 2018
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Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved?
Olivier Bernard, Alain Lalande, Clement Zotti, Frederick Cervenansky, Xin Yang, Pheng-Ann Heng, Irem Cetin, Karim Lekadir, Oscar Camara, Miguel Angel Gonzalez Ballester, et al · 2018
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Adversarial contrastive estimation
Avishek Joey Bose, Huan Ling, and Yanshuai Cao · 2018
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Deep clustering for unsupervised learning of visual features
Unsupervised domain adaptation for medical imaging segmentation with self-ensembling
Christian S Perone, Pedro Ballester, Rodrigo C Barros, and Julien Cohen-Adad · 2019
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On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aaron van den Oord, Alexander A Alemi, and George Tucker · 2019
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Understanding the limitations of variational mutual information estimators
Jiaming Song and Stefano Ermon · 2019
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Multimodal self-supervised learning for medical image analysis
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Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2018
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Invariant information distillation for unsupervised image segmentation and clustering
Xu Ji, João F. Henriques, and Andrea Vedaldi · 2018
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A robust deep attention network to noisy labels in semi-supervised biomedical segmentation
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T. Miyato, S. Maeda, M. Koyama, and S. Ishii · 2018
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Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
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Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, and Patrick Pérez · 2019
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