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Recent studies on contrastive learning have achieved remarkable performance solely by leveraging few labels in the context of medical image segmentation.
P. L. Bartlett and S. Mendelson, “Rademacher and gaussian complexities: Risk bounds and structural results,” Journal of Machine Learning Research , vol. 3, no. Nov, pp. 463–482, 2002
2002
Earlier work this paper cites.
Y. Grandvalet and Y. Bengio, “Semi-supervised learning by entropy minimization,” NeurIPS , 2004
2004
Earlier work this paper cites.
R. Hadsell, S. Chopra, and Y. LeCun, “Dimensionality reduction by learning an invariant mapping,” in CVPR , 2006
2006
Earlier work this paper cites.
D.-H. Lee et al. , “Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks,” in Workshop on challenges in representation learning, ICML , 2013
2013
Earlier work this paper cites.
X. Zhu, D. Anguelov, and D. Ramanan, “Capturing long-tail distributions of object subcategories,” in CVPR , 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in CVPR , 2015
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in MICCAI , 2015
2015
Earlier work this paper cites.
C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra, “Weight uncertainty in neural network,” in ICML , 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016
2016
Earlier work this paper cites.
F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 3DV . IEEE, 2016
2016
Earlier work this paper cites.
Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in ICML , 2016
2016
Earlier work this paper cites.
M. Sajjadi, M. Javanmardi, and T. Tasdizen, “Regularization with stochastic transformations and perturbations for deep semi-supervised learning,” in NeurIPS , 2016
2016
Earlier work this paper cites.
X. Zhuang and J. Shen, “Multi-scale patch and multi-modality atlases for whole heart segmentation of mri,” Medical image analysis , 2016
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in NeurIPS , 2017
2017
Earlier work this paper cites.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE transactions on pattern analysis and machine intelligence , 2017
2017
Earlier work this paper cites.
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in CVPR , 2017
2017
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in ICCV , 2017
2017
Earlier work this paper cites.
W. Bai, O. Oktay, M. Sinclair, H. Suzuki, M. Rajchl, G. Tarroni, B. Glocker, A. King, P. M. Matthews, and D. Rueckert, “Semi-supervised learning for network-based cardiac mr image segmentation,” in MICCAI , 2017
2017
Earlier work this paper cites.
A. Kendall and Y. Gal, “What uncertainties do we need in bayesian deep learning for computer vision?” in NeurIPS , 2017
2017
Earlier work this paper cites.
A. Tarvainen and H. Valpola, “Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,” in NeurIPS , 2017
2017
Earlier work this paper cites.
Y. Zhang, L. Yang, J. Chen, M. Fredericksen, D. P. Hughes, and D. Z. Chen, “Deep adversarial networks for biomedical image segmentation utilizing unannotated images,” in MICCAI , 2017
2017
Earlier work this paper cites.
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in CVPR , 2017
2017
Earlier work this paper cites.
Z. Wu, Y. Xiong, X. Y. Stella, and D. Lin, “Unsupervised feature learning via non-parametric instance discrimination,” in CVPR , 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in ECCV , 2018
2018
Earlier work this paper cites.
S. Qiao, W. Shen, Z. Zhang, B. Wang, and A. Yuille, “Deep co-training for semi-supervised image recognition,” in ECCV , 2018
2018
Earlier work this paper cites.
D. Nie, Y. Gao, L. Wang, and D. Shen, “Asdnet: Attention based semi-supervised deep networks for medical image segmentation,” in MICCAI , 2018
2018
Earlier work this paper cites.
Z. Zhang, L. Yang, and Y. Zheng, “Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network,” in CVPR , 2018
2018
Earlier work this paper cites.
O. Bernard, A. Lalande, C. Zotti, F. Cervenansky, X. Yang, P.-A. Heng, I. Cetin, K. Lekadir, O. Camara, M. A. G. Ballester et al. , “Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved?” IEEE Transactions on Medical Imaging , 2018
2018
Earlier work this paper cites.
X. Li, H. Chen, X. Qi, Q. Dou, C.-W. Fu, and P.-A. Heng, “H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes,” IEEE Trans. Med. Imaging , 2018
2018
Earlier work this paper cites.
F. Perez, C. Vasconcelos, S. Avila, and E. Valle, “Data augmentation for skin lesion analysis,” in OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis . Springer, 2018, pp. 303–311
2018
Earlier work this paper cites.
T. Furlanello, Z. Lipton, M. Tschannen, L. Itti, and A. Anandkumar, “Born again neural networks,” in ICML , 2018
2018
Earlier work this paper cites.
R. D. Hjelm, A. Fedorov, S. Lavoie-Marchildon, K. Grewal, P. Bachman, A. Trischler, and Y. Bengio, “Learning deep representations by mutual information estimation and maximization,” in ICLR , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Y. Cui, M. Jia, T.-Y. Lin, Y. Song, and S. Belongie, “Class-balanced loss based on effective number of samples,” in CVPR , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
J. Li, P. Zhou, C. Xiong, and S. Hoi, “Prototypical contrastive learning of unsupervised representations,” in ICLR , 2021
2021
Later among the works it cites.
Z. Jiang, T. Chen, T. Chen, and Z. Wang, “Improving contrastive learning on imbalanced data via open-world sampling,” in NeurIPS , 2021
2021
Later among the works it cites.
M. Zheng, S. You, F. Wang, C. Qian, C. Zhang, X. Wang, and C. Xu, “Ressl: Relational self-supervised learning with weak augmentation,” in NeurIPS , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
W. Van Gansbeke, S. Vandenhende, S. Georgoulis, and L. V. Gool, “Revisiting contrastive methods for unsupervised learning of visual representations,” in NeurIPS , 2021
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L. Yu, S. Wang, X. Li, C.-W. Fu, and P.-A. Heng, “Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation,” in MICCAI , 2019
2019
Cited alongside, same era.
S. Graham, H. Chen, J. Gamper, Q. Dou, P.-A. Heng, D. Snead, Y. W. Tsang, and N. Rajpoot, “Mild-net: Minimal information loss dilated network for gland instance segmentation in colon histology images,” Medical image analysis , 2019
2019
Cited alongside, same era.
G. Bortsova, F. Dubost, L. Hogeweg, I. Katramados, and M. de Bruijne, “Semi-supervised medical image segmentation via learning consistency under transformations,” in MICCAI , 2019
2019
Cited alongside, same era.
W. Cui, Y. Liu, Y. Li, M. Guo, Y. Li, X. Li, T. Wang, X. Zeng, and C. Ye, “Semi-supervised brain lesion segmentation with an adapted mean teacher model,” in MICCAI , 2019
2019
Cited alongside, same era.
Y. Zhou, Y. Wang, P. Tang, S. Bai, W. Shen, E. Fishman, and A. Yuille, “Semi-supervised 3d abdominal multi-organ segmentation via deep multi-planar co-training,” in WACV , 2019
2019
Cited alongside, same era.
H. Zheng, L. Lin, H. Hu, Q. Zhang, Q. Chen, Y. Iwamoto, X. Han, Y.-W. Chen, R. Tong, and J. Wu, “Semi-supervised segmentation of liver using adversarial learning with deep atlas prior,” in MICCAI , 2019
2019
Cited alongside, same era.
T.-H. Vu, H. Jain, M. Bucher, M. Cord, and P. Pérez, “Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation,” in CVPR , 2019
2019
Cited alongside, same era.
V. Verma, K. Kawaguchi, A. Lamb, J. Kannala, Y. Bengio, and D. Lopez-Paz, “Interpolation consistency training for semi-supervised learning,” in IJCAI , 2019
2019
Cited alongside, same era.
2021
Later among the works it cites.
2021
Later among the works it cites.
A. Tejankar, S. A. Koohpayegani, V. Pillai, P. Favaro, and H. Pirsiavash, “Isd: Self-supervised learning by iterative similarity distillation,” in ICCV , 2021
2021
Later among the works it cites.
J. Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, L. Lu, A. L. Yuille, and Y. Zhou, “Transunet: Transformers make strong encoders for medical image segmentation,” in MICCAI , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
Y. Xie, J. Zhang, C. Shen, and Y. Xia, “Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation,” in MICCAI , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
J. M. J. Valanarasu, P. Oza, I. Hacihaliloglu, and V. M. Patel, “Medical transformer: Gated axial-attention for medical image segmentation,” in MICCAI , 2021
2021
Later among the works it cites.
G. Shi, L. Xiao, Y. Chen, and S. K. Zhou, “Marginal loss and exclusion loss for partially supervised multi-organ segmentation,” Medical Image Analysis , 2021
2021
Later among the works it cites.
X. Chen, Y. Yuan, G. Zeng, and J. Wang, “Semi-supervised semantic segmentation with cross pseudo supervision,” in CVPR , 2021
2021
Later among the works it cites.
S. Reiß, C. Seibold, A. Freytag, E. Rodner, and R. Stiefelhagen, “Every annotation counts: Multi-label deep supervision for medical image segmentation,” in CVPR , 2021
2021
Later among the works it cites.
G. Valvano, A. Leo, and S. A. Tsaftaris, “Learning to segment from scribbles using multi-scale adversarial attention gates,” IEEE Transactions on Medical Imaging , 2021
2021
Later among the works it cites.
D. Dwibedi, Y. Aytar, J. Tompson, P. Sermanet, and A. Zisserman, “With a little help from my friends: Nearest-neighbor contrastive learning of visual representations,” in ICCV , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
X. Hu, D. Zeng, X. Xu, and Y. Shi, “Semi-supervised contrastive learning for label-efficient medical image segmentation,” in MICCAI , 2021
2021
Later among the works it cites.
A. Galdran, G. Carneiro, and M. A. González Ballester, “Balanced-mixup for highly imbalanced medical image classification,” in MICCAI , 2021
2021
Later among the works it cites.
X. Luo, W. Liao, J. Chen, T. Song, Y. Chen, S. Zhang, N. Chen, G. Wang, and S. Zhang, “Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency,” in MICCAI , 2021
2021
Later among the works it cites.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
Z. Zhao, F. Zhou, Z. Zeng, C. Guan, and S. K. Zhou, “Meta-hallucinator: Towards few-shot cross-modality cardiac image segmentation,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part V . Springer, 2022, pp. 128–139
2022
Closest in time.
C. You, Y. Zhou, R. Zhao, L. Staib, and J. S. Duncan, “Simcvd: Simple contrastive voxel-wise representation distillation for semi-supervised medical image segmentation,” IEEE Transactions on Medical Imaging , 2022
2022
Closest in time.
R. Dangovski, L. Jing, C. Loh, S. Han, A. Srivastava, B. Cheung, P. Agrawal, and M. Soljacic, “Equivariant contrastive learning,” in ICLR , 2022
2022
Closest in time.
K. Yan, J. Cai, D. Jin, S. Miao, D. Guo, A. P. Harrison, Y. Tang, J. Xiao, J. Lu, and L. Lu, “Sam: Self-supervised learning of pixel-wise anatomical embeddings in radiological images,” IEEE Trans. Med. Imaging , 2022
2022
Closest in time.
A. G. Roy, J. Ren, S. Azizi, A. Loh, V. Natarajan, B. Mustafa, N. Pawlowski, J. Freyberg, Y. Liu, Z. Beaver et al. , “Does your dermatology classifier know what it doesn’t know? detecting the long-tail of unseen conditions,” Medical Image Analysis , 2022
2022
Closest in time.
Y. Wu, Z. Wu, Q. Wu, Z. Ge, and J. Cai, “Exploring smoothness and class-separation for semi-supervised medical image segmentation,” in MICCAI , 2022
2022
Closest in time.
I. Nassar, M. Hayat, E. Abbasnejad, H. Rezatofighi, and G. Haffari, “Protocon: Pseudo-label refinement via online clustering and prototypical consistency for efficient semi-supervised learning,” in CVPR , 2023
2023
Closest in time.
S. Zhang, J. Zhang, B. Tian, T. Lukasiewicz, and Z. Xu, “Multi-modal contrastive mutual learning and pseudo-label re-learning for semi-supervised medical image segmentation,” Medical Image Analysis , 2023
2023
Closest in time.
A. Lou, K. Tawfik, X. Yao, Z. Liu, and J. Noble, “Min-max similarity: A contrastive semi-supervised deep learning network for surgical tools segmentation,” IEEE Transactions on Medical Imaging , 2023
2023
Closest in time.