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Masked autoencoder (MAE) is a promising self-supervised pre-training technique that can improve the representation learning of a neural network without human intervention.
Simpson, A. L.; Antonelli, M.; Bakas, S.; Bilello, M.; Farahani, K.; Van Ginneken, B.; Kopp-Schneider, A.; Landman, B. A.; Litjens, G.; Menze, B.; et al. 2019 · 1902
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Masked image modeling advances 3d medical image analysis
Chen, Z.; Agarwal, D.; Aggarwal, K.; Safta, W.; Balan, M. M.; and Brown, K. 2023 · 1980
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. 2020 · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P.; Larochelle, H.; Lajoie, I.; Bengio, Y.; Manzagol, P.-A.; and Bottou, L. 2010 · 2010
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Exploring Simple Siamese Representation Learning
Chen, X.; and He, K. 2020 · 2011
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PGL: prior-guided local self-supervised learning for 3D medical image segmentation
Xie, Y.; Zhang, J.; Liao, Z.; Xia, Y.; and Shen, C. 2020 · 2011
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Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
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Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge
Landman, B.; Xu, Z.; Igelsias, J.; Styner, M.; Langerak, T.; and Klein, A. 2015 · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O.; Fischer, P.; and Brox, T. 2015 · 2015
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Gaussian error linear units (gelus)
Hendrycks, D.; and Gimpel, K. 2016 · 2016
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Context encoders: Feature learning by inpainting
Pathak, D.; Krahenbuhl, P.; Donahue, J.; Darrell, T.; and Efros, A. A. 2016 · 2016
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Deep learning in medical image analysis
Shen, D.; Wu, G.; and Suk, H.-I. 2017 · 2017
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3D MRI brain tumor segmentation using autoencoder regularization
Myronenko, A. 2019 · 2018
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Multivariate mixture model for myocardial segmentation combining multi-source images
Zhuang, X. 2018 · 2018
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Self-supervised learning for medical image analysis using image context restoration
Chen, L.; Bentley, P.; Mori, K.; Misawa, K.; Fujiwara, M.; and Rueckert, D. 2019 · 2019
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Ct images in covid-19 dataset
An, P.; Xu, S.; Harmon, S.; Turkbey, E. B.; Sanford, T. H.; Amalou, A.; Kassin, M.; Varble, N.; Blain, M.; Anderson, V.; et al. 2020 · 2020
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Contrastive learning of global and local features for medical image segmentation with limited annotations
Chaitanya, K.; Erdil, E.; Karani, N.; and Konukoglu, E. 2020 · 2020
Cited alongside, same era.
Momentum Contrast for Unsupervised Visual Representation Learning
He, K.; Fan, H.; Wu, Y.; Xie, S.; and Girshick, R. 2020 · 2020
Cited alongside, same era.
Self-supervised visual feature learning with deep neural networks: A survey
Jing, L.; and Tian, Y. 2020 · 2020
Cited alongside, same era.
Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
Tajbakhsh, N.; Jeyaseelan, L.; Li, Q.; Chiang, J. N.; Wu, Z.; and Ding, X. 2020 · 2020
Cited alongside, same era.
3d self-supervised methods for medical imaging
Taleb, A.; Loetzsch, W.; Danz, N.; Severin, J.; Gaertner, T.; Bergner, B.; and Lippert, C. 2020 · 2020
Cited alongside, same era.
DisCo: Remedying Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning
Gao, Y.; Zhuang, J.-X.; Lin, S.; Cheng, H.; Sun, X.; Li, K.; and Shen, C. 2022 · 2022
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DiRA: discriminative, restorative, and adversarial learning for self-supervised medical image analysis
Haghighi, F.; Taher, M. R. H.; Gotway, M. B.; and Liang, J. 2022 · 2022
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UNETR : Transformers for 3d medical image segmentation
Hatamizadeh, A.; Tang, Y.; Nath, V.; Yang, D.; Myronenko, A.; Landman, B.; Roth, H. R.; and Xu, D. 2022 · 2022
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Masked autoencoders are scalable vision learners
He, K.; Chen, X.; Xie, S.; Li, Y.; Dollár, P.; and Girshick, R. 2022 · 2022
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Rapid artificial intelligence solutions in a pandemic—The COVID-19-20 Lung CT Lesion Segmentation Challenge
Roth, H. R.; Xu, Z.; Tor-Díez, C.; Jacob, R. S.; Zember, J.; Molto, J.; Li, W.; Xu, S.; Turkbey, B.; Turkbey, E.; et al. 2022 · 2022
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Revisiting Rubik’s cube: self-supervised learning with volume-wise transformation for 3D medical image segmentation
Tao, X.; Li, Y.; Zhou, W.; Ma, K.; and Zheng, Y. 2020 · 2020
Cited alongside, same era.
Rubik’s cube+: A self-supervised feature learning framework for 3d medical image analysis
Zhu, J.; Li, Y.; Hu, Y.; Ma, K.; Zhou, S. K.; and Zheng, Y. 2020 · 2020
Cited alongside, same era.
Big self-supervised models advance medical image classification
Azizi, S.; Mustafa, B.; Ryan, F.; Beaver, Z.; Freyberg, J.; Deaton, J.; Loh, A.; Karthikesalingam, A.; Kornblith, S.; Chen, T.; et al. 2021 · 2021
Cited alongside, same era.
Emerging properties in self-supervised vision transformers
Caron, M.; Touvron, H.; Misra, I.; Jégou, H.; Mairal, J.; Bojanowski, P.; and Joulin, A. 2021 · 2021
Cited alongside, same era.
Improved baselines with momentum contrastive learning
Chen, X.; Fan, H.; Girshick, R.; and He, K. 2021 · 2021
Cited alongside, same era.
Swin transformer: Hierarchical vision transformer using shifted windows
Liu, Z.; Lin, Y.; Cao, Y.; Hu, H.; Wei, Y.; Zhang, Z.; Lin, S.; and Guo, B. 2021 · 2021
Cited alongside, same era.
Self-supervised graph-level representation learning with local and global structure
Xu, M.; Wang, H.; Ni, B.; Guo, H.; and Tang, J. 2021 · 2021
Cited alongside, same era.
Self-supervised pre-training of swin transformers for 3d medical image analysis
Tang, Y.; Yang, D.; Li, W.; Roth, H. R.; Landman, B.; Xu, D.; Nath, V.; and Hatamizadeh, A. 2022 · 2022
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Contrastive learning with stronger augmentations
Wang, X.; and Qi, G.-J. 2022 · 2022
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SimMIM: A simple framework for masked image modeling
Xie, Z.; Zhang, Z.; Cao, Y.; Lin, Y.; Bao, J.; Yao, Z.; Dai, Q.; and Hu, H. 2022 · 2022
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DeSD: Self-Supervised Learning with Deep Self-Distillation for 3D Medical Image Segmentation
Ye, Y.; Zhang, J.; Chen, Z.; and Xia, Y. 2022 · 2022
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Leverage your local and global representations: A new self-supervised learning strategy
Zhang, T.; Qiu, C.; Ke, W.; Süsstrunk, S.; and Salzmann, M. 2022 · 2022
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Geometric Visual Similarity Learning in 3D Medical Image Self-supervised Pre-training
He, Y.; Yang, G.; Ge, R.; Chen, Y.; Coatrieux, J.-L.; Wang, B.; and Li, S. 2023 · 2023
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Label-efficient deep learning in medical image analysis: Challenges and future directions
Jin, C.; Guo, Z.; Lin, Y.; Luo, L.; and Chen, H. 2023 · 2023
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Joint self-supervised image-volume representation learning with intra-inter contrastive clustering
Nguyen, D. M.; Nguyen, H.; Mai, T. T.; Cao, T.; Nguyen, B. T.; Ho, N.; Swoboda, P.; Albarqouni, S.; Xie, P.; and Sonntag, D. 2023 · 2023
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The effectiveness of MAE pre-pretraining for billion-scale pretraining
Singh, M.; Duval, Q.; Alwala, K. V.; Fan, H.; Aggarwal, V.; Adcock, A.; Joulin, A.; Dollár, P.; Feichtenhofer, C.; Girshick, R.; et al. 2023 · 2023
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A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis
Zhou, H.-Y.; Lu, C.; Chen, C.; Yang, S.; and Yu, Y. 2023 · 2023
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