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Lesion detection in brain Magnetic Resonance Images (MRI) remains a challenging task.
A brain tumor segmentation framework based on outlier detection
M Prastawa, E Bullitt, S Ho, and G Gerig · 2004
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Learning object motion patterns for anomaly detection and improved object detection
Arslan Basharat, Alexei Gritai, and Mubarak Shah · 2008
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Brain tumor segmentation using support vector machines, symbolic and quantitative approaches to reasoning with uncertainty
R Ayachi and N Ben Amor · 2009
Earlier work this paper cites.
Context-sensitive classification forests for segmentation of brain tumor tissues
D Zikic, B Glocker, E Konukoglu, J Shotton, A Criminisi, D Ye, C Demiralp, OM Thomas, T Das, R Jena, et al · 2012
Earlier work this paper cites.
Segmentation of brain tumor images based on integrated hierarchical classification and regularization
Stefan Bauer, Thomas Fejes, Johannes Slotboom, Roland Wiest, Lutz-P Nolte, and Mauricio Reyes · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Cited alongside, same era.
Brain tumor segmentation using convolutional neural networks in mri images
Sérgio Pereira, Adriano Pinto, Victor Alves, and Carlos A Silva · 2016
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2016
Cited alongside, same era.
Deep unsupervised clustering with gaussian mixture variational autoencoders
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Unsupervised real-time anomaly detection for streaming data
Subutai Ahmad, Alexander Lavin, Scott Purdy, and Zuha Agha · 2017
Later among the works it cites.
Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs · 2017
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B Ravi Kiran, Dilip Mathew Thomas, and Ranjith Parakkal · 2018
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Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications
Haowen Xu, Wenxiao Chen, Nengwen Zhao, Zeyan Li, Jiahao Bu, Zhihan Li, Ying Liu, Youjian Zhao, Dan Pei, Yang Feng, et al · 2018
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Nat Dilokthanakul, Pedro AM Mediano, Marta Garnelo, Matthew CH Lee, Hugh Salimbeni, Kai Arulkumaran, and Murray Shanahan · 2016
Cited alongside, same era.
Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation
Konstantinos Kamnitsas, Christian Ledig, Virginia FJ Newcombe, Joanna P Simpson, Andrew D Kane, David K Menon, Daniel Rueckert, and Ben Glocker · 2017
Cited alongside, same era.
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
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A primitive study on unsupervised anomaly detection with an autoencoder in emergency head ct volumes
Daisuke Sato, Shouhei Hanaoka, Yukihiro Nomura, Tomomi Takenaga, Soichiro Miki, Takeharu Yoshikawa, Naoto Hayashi, and Osamu Abe · 2018
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