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Neural networks achieve state-of-the-art performance in many supervised learning tasks when the training data distribution matches the test data distribution.
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26
1945
Earlier work this paper cites.
Brett, M., Johnsrude, I.S., Owen, A.M.: The problem of functional localization in the human brain. Nat Rev Neurosci 3
2002
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
2009
Earlier work this paper cites.
Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.: N4itk: improved n3 bias correction. IEEE transactions on medical imaging 29
2010
Earlier work this paper cites.
Fischl, B.: Freesurfer. Neuroimage 62
2012
Earlier work this paper cites.
Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., Ugurbil, K., Consortium, W.M.H., et al.: The wu-minn human connectome project: an overview. Neuroimage 80
2013
Earlier work this paper cites.
Di Martino, A., Yan, C.G., Li, Q., Denio, E., Castellanos, F.X., Alaerts, K., Anderson, J.S., Assaf, M., Bookheimer, S.Y., Dapretto, M., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry 19
2014
Earlier work this paper cites.
Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34
2014
Earlier work this paper cites.
Van Opbroek, A., Ikram, M.A., Vernooij, M.W., De Bruijne, M.: Transfer learning improves supervised image segmentation across imaging protocols. IEEE transactions on medical imaging 34
2014
Earlier work this paper cites.
Bloch, N., Madabhushi, A., Huisman, H., Freymann, J., Kirby, J., Grauer, M., Enquobahrie, A., Jaffe, C., Clarke, L., Farahani, K.: Nci-isbi 2013 challenge: automated segmentation of prostate structures. 2015. DOI: https://doi. org/10.7937 K 9
2015
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. pp. 234–241. Springer (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Liang, J.: Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging 35
2016
Earlier work this paper cites.
Kamnitsas, K., Baumgartner, C., Ledig, C., Newcombe, V., Simpson, J., Kane, A., Menon, D., Nori, A., Criminisi, A., Rueckert, D., et al.: Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In: Information Processing in Medical Imaging: 25th International Conference, IPMI 2017, Boone, NC, USA, June 25-30, 2017, Proceedings 25. pp. 597–609. Springer (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
Huo, Y., Xu, Z., Moon, H., Bao, S., Assad, A., Moyo, T.K., Savona, M.R., Abramson, R.G., Landman, B.A.: Synseg-net: Synthetic segmentation without target modality ground truth. IEEE transactions on medical imaging 38
2018
Earlier work this paper cites.
Karani, N., Chaitanya, K., Baumgartner, C., Konukoglu, E.: A lifelong learning approach to brain mr segmentation across scanners and protocols. In: International conference on medical image computing and computer-assisted intervention. pp. 476–484. Springer (2018)
2018
Earlier work this paper cites.
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training (2018)
2018
Earlier work this paper cites.
Al-Kafri, A.S., Sudirman, S., Hussain, A., Al-Jumeily, D., Natalia, F., Meidia, H., Afriliana, N., Al-Rashdan, W., Bashtawi, M., Al-Jumaily, M.: Boundary delineation of mri images for lumbar spinal stenosis detection through semantic segmentation using deep neural networks. IEEE Access 7
2019
Earlier work this paper cites.
Dou, Q., Coelho de Castro, D., Kamnitsas, K., Glocker, B.: Domain generalization via model-agnostic learning of semantic features. Advances in neural information processing systems 32
2019
Earlier work this paper cites.
Dou, Q., Ouyang, C., Chen, C., Chen, H., Glocker, B., Zhuang, X., Heng, P.A.: Pnp-adanet: Plug-and-play adversarial domain adaptation network at unpaired cross-modality cardiac segmentation. IEEE Access 7
2019
Earlier work this paper cites.
Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., Lu, H.: Dual attention network for scene segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 3146–3154 (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Ouyang, C., Kamnitsas, K., Biffi, C., Duan, J., Rueckert, D.: Data efficient unsupervised domain adaptation for cross-modality image segmentation. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II 22. pp. 669–677. Springer (2019)
2019
Cited alongside, same era.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1
Hoyer, L., Dai, D., Van Gool, L.: Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 9924–9935 (2022)
2022
Later among the works it cites.
Li, J., Li, D., Xiong, C., Hoi, S.: Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In: International conference on machine learning. pp. 12888–12900. PMLR (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
Butoi, V.I., Ortiz, J.J.G., Ma, T., Sabuncu, M.R., Guttag, J., Dalca, A.V.: Universeg: Universal medical image segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 21438–21451 (2023)
2023
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2019
Cited alongside, same era.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33
2020
Cited alongside, same era.
Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E.: Contrastive learning of global and local features for medical image segmentation with limited annotations. Advances in neural information processing systems 33
2020
Cited alongside, same era.
Contributors, M.: MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark. https://github.com/open-mmlab/mmsegmentation (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2021
Cited alongside, same era.
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 9650–9660 (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Later among the works it cites.
2023
Later among the works it cites.
Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E.: Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation. Medical image analysis 87
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., et al.: Segment anything. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4015–4026 (2023)
2023
Later among the works it cites.
Mazurowski, M.A., Dong, H., Gu, H., Yang, J., Konz, N., Zhang, Y.: Segment anything model for medical image analysis: an experimental study. Medical Image Analysis 89
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Cheng, Z., Wei, Q., Zhu, H., Wang, Y., Qu, L., Shao, W., Zhou, Y.: Unleashing the potential of sam for medical adaptation via hierarchical decoding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3511–3522 (2024)
2024
Closest in time.
Ke, L., Ye, M., Danelljan, M., Tai, Y.W., Tang, C.K., Yu, F., et al.: Segment anything in high quality. Advances in Neural Information Processing Systems 36
2024
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Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nature Communications 15
2024
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Valanarasu, J.M.J., Guo, P., Vibashan, V., Patel, V.M.: On-the-fly test-time adaptation for medical image segmentation. In: Medical Imaging with Deep Learning. pp. 586–598. PMLR (2024)
2024
Closest in time.
Wei, Z., Chen, L., Jin, Y., Ma, X., Liu, T., Ling, P., Wang, B., Chen, H., Zheng, J.: Stronger fewer & superior: Harnessing vision foundation models for domain generalized semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 28619–28630 (2024)
2024
Closest in time.
Zou, X., Yang, J., Zhang, H., Li, F., Li, L., Wang, J., Wang, L., Gao, J., Lee, Y.J.: Segment everything everywhere all at once. Advances in Neural Information Processing Systems 36
2024
Closest in time.