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Self-supervised learning methods have witnessed a recent surge of interest after proving successful in multiple application fields.
Self-supervised visual feature learning with deep neural networks: A survey
Longlong Jing and Yingli Tian · 1902
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Data-efficient image recognition with contrastive predictive coding
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
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Carl Doersch, Abhinav Gupta, and Alexei A. Efros · 2015
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Unsupervised learning of visual representations using videos
Xiaolong Wang and Abhinav Gupta · 2015
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Anticipating the future by watching unlabeled video
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2015
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Dense optical flow prediction from a static image
Jacob Walker, Abhinav Gupta, and Martial Hebert · 2015
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Parallel multi-dimensional lstm, with application to fast biomedical volumetric image segmentation
Marijn F. Stollenga, Wonmin Byeon, Marcus Liwicki, and Jürgen Schmidhuber · 2015
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Unsupervised learning of visual representations using videos
Xiaolong Wang and Abhinav Gupta · 2015
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Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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The multimodal brain tumor image segmentation benchmark (brats)
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Uk biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age
Cathie Sudlow, John Gallacher, Naomi Allen, Valerie Beral, Paul Burton, John Danesh, Paul Downey, Paul Elliott, Jane Green, Martin Landray, Bette Liu, Paul Matthews, Giok Ong, Jill Pell, Alan Silman, Alan Young, Tim Sprosen, Tim Peakman, and Rory Collins · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Diederik P. Kingma and Jimmy Ba · 2015
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Unsupervised learning of visual representations by solving jigsaw puzzles
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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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krähenbühl, Jeff Donahue, Trevor Darrell, and Alexei Efros · 2016
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Pose from action: Unsupervised learning of pose features based on motion
Senthil Purushwalkam and Abhinav Gupta · 2016
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Conditional image generation with pixelcnn decoders
Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, koray kavukcuoglu, Oriol Vinyals, and Alex Graves · 2016
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Pixel recurrent neural networks
Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Deep learning advances in computer vision with 3d data: A survey
Anastasia Ioannidou, Elisavet Chatzilari, Spiros Nikolopoulos, and Ioannis Kompatsiaris · 2017
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Annotating Medical Image Data , pages 45–67
Context aware 3-d residual networks for brain tumor segmentation
Siddhartha Chandra, Maria Vakalopoulou, Lucas Fidon, Enzo Battistella, Theo Estienne, Roger Sun, Charlotte Robert, Eric Deutch, and Nikos Paragios · 2018
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Deep learning fundus image analysis for diabetic retinopathy and macular edema grading
Jaakko Sahlsten, Joel Jaskari, Jyri Kivinen, Lauri Turunen, Esa Jaanio, Kustaa Hietala, and Kimmo Kaski · 2019
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Transfusion: Understanding transfer learning for medical imaging
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, and Samy Bengio · 2019
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The retrieval of the beautiful: Self-supervised salient object detection for beauty product retrieval
Jiawei Wang, Shuai Zhu, Jiao Xu, and Da Cao · 2019
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Self-supervised video representation learning with space-time cubic puzzles
Kim Dahun, Donghyeon Cho, and Soo-Ok Kweon · 2019
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Katharina Grünberg, Oscar Jimenez-del Toro, Andras Jakab, Georg Langs, Tomàs Salas Fernandez, Marianne Winterstein, Marc-André Weber, and Markus Krenn · 2017
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Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M. Summers · 2017
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Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning
Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Yi Ding, Aarti Bagul, Curtis Langlotz, Katie S. Shpanskaya, Matthew P. Lungren, and Andrew Y. Ng · 2017
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Self-supervised siamese learning on stereo image pairs for depth estimation in robotic surgery
Menglong Ye, Edward Johns, Ankur Handa, Lin Zhang, Philip Pratt, and Guang Yang · 2017
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Self supervised deep representation learning for fine-grained body part recognition
Pengyue Zhang, Fusheng Wang, and Yefeng Zheng · 2017
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Self-supervised learning for spinal mris
Amir Jamaludin, Timor Kadir, and Andrew Zisserman · 2017
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Exploiting the potential of unlabeled endoscopic video data with self-supervised learning
Tobias Roß, David Zimmerer, Anant Vemuri, Fabian Isensee, Sebastian Bodenstedt, Fabian Both, Philip Kessler, Martin Wagner, Beat Müller, Hannes Kenngott, Stefanie Speidel, Klaus Maier-Hein, and Lena Maier-Hein · 2017
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Self-supervised learning for cardiac mr image segmentation by anatomical position prediction
Wenjia Bai, Chen Chen, Giacomo Tarroni, Jinming Duan, Florian Guitton, Steffen E. Petersen, Yike Guo, Paul M. Matthews, and Daniel Rueckert · 2019
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Deep Lesion Graph in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database , pages 413–435
Ke Yan, Xiaosong Wang, Le Lu, Ling Zhang, Adam P. Harrison, Mohammadhadi Bagheri, and Ronald M. Summers · 2019
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Surrogate supervision for medical image analysis: Effective deep learning from limited quantities of labeled data
N. Tajbakhsh, Y. Hu, J. Cao, X. Yan, Y. Xiao, Y. Lu, J. Liang, D. Terzopoulos, and X. Ding · 2019
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Self-supervised learning for medical image analysis using image context restoration
Liang Chen, Paul Bentley, Kensaku Mori, Kazunari Misawa, Michitaka Fujiwara, and Daniel Rueckert · 2019
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Multimodal self-supervised learning for medical image analysis, 2019
Aiham Taleb, Christoph Lippert, Tassilo Klein, and Moin Nabi · 2019
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How to learn from unlabeled volume data: Self-supervised 3d context feature learning
Maximilian Blendowski, Hannes Nickisch, and Mattias P. Heinrich · 2019
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Models genesis: Generic autodidactic models for 3d medical image analysis
Zongwei Zhou, Vatsal Sodha, Md Mahfuzur Rahman Siddiquee, Ruibin Feng, Nima Tajbakhsh, Michael B. Gotway, and Jianming Liang · 2019
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Self-supervised feature learning for 3d medical images by playing a rubik’s cube
Xinrui Zhuang, Yuexiang Li, Yifan Hu, Kai Ma, Yujiu Yang, and Yefeng Zheng · 2019
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Scaling and benchmarking self-supervised visual representation learning
Priya Goyal, Dhruv Mahajan, Harikrishna Mulam, and Ishan Misra · 2019
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Amber L. Simpson, Michela Antonelli, Spyridon Bakas, Michel Bilello, Keyvan Farahani, Bram van Ginneken, Annette Kopp-Schneider, Bennett A. Landman, Geert J. S. Litjens, Bjoern H. Menze, Olaf Ronneberger, Ronald M. Summers, Patrick Bilic, Patrick Ferdinand Christ, Richard K. G. Do, Marc Gollub, Jennifer Golia-Pernicka, Stephan Heckers, William R. Jarnagin, Maureen McHugo, Sandy Napel, Eugene Vorontsov, Lena Maier-Hein, and M. Jorge Cardoso · 2019
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Revisiting self-supervised visual representation learning
Alexander Kolesnikov, Xiaohua Zhai, and Lucas Beyer · 2019
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Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Momentum contrast for unsupervised visual representation learning
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Self-supervised representation learning for ultrasound video
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Contrastive learning of global and local features for medical image segmentation with limited annotations, 2020
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Rubik’s cube+: A self-supervised feature learning framework for 3d medical image analysis
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