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Medical image segmentation plays a crucial role in advancing healthcare systems for disease diagnosis and treatment planning.
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2015
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2015
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2016
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2017
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Y. Zhou, L. Xie, W. Shen, Y. Wang, E. K. Fishman, and A. L. Yuille, “A fixed-point model for pancreas segmentation in abdominal ct scans,” in International conference on medical image computing and computer-assisted intervention . Springer, 2017, pp. 693–701
2017
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2017
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2018
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Q. Yu, L. Xie, Y. Wang, Y. Zhou, E. K. Fishman, and A. L. Yuille, “Recurrent saliency transformation network: Incorporating multi-stage visual cues for small organ segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 8280–8289
2018
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Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “Unet++: A nested u-net architecture for medical image segmentation,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . Springer, 2018, pp. 3–11
2018
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2018
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N. Parmar, A. Vaswani, J. Uszkoreit, L. Kaiser, N. Shazeer, A. Ku, and D. Tran, “Image transformer,” in International Conference on Machine Learning . PMLR, 2018, pp. 4055–4064
2018
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Z. Zhu, Y. Xia, W. Shen, E. Fishman, and A. Yuille, “A 3d coarse-to-fine framework for volumetric medical image segmentation,” in 2018 International conference on 3D vision (3DV) . IEEE, 2018, pp. 682–690
2018
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2018
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L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 801–818
2018
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J. Schlemper, O. Oktay, M. Schaap, M. Heinrich, B. Kainz, B. Glocker, and D. Rueckert, “Attention gated networks: Learning to leverage salient regions in medical images,” Medical image analysis , vol. 53, pp. 197–207, 2019
2019
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2019
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L. Xie, Q. Yu, Y. Zhou, Y. Wang, E. K. Fishman, and A. L. Yuille, “Recurrent saliency transformation network for tiny target segmentation in abdominal ct scans,” IEEE transactions on medical imaging , vol. 39, no. 2, pp. 514–525, 2019
2019
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Z. Zhu, Y. Xia, L. Xie, E. K. Fishman, and A. L. Yuille, “Multi-scale coarse-to-fine segmentation for screening pancreatic ductal adenocarcinoma,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI 22 . Springer, 2019, pp. 3–12
H. Wang, Y. Zhu, H. Adam, A. Yuille, and L.-C. Chen, “Max-deeplab: End-to-end panoptic segmentation with mask transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 5463–5474
2021
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B. Cheng, A. Schwing, and A. Kirillov, “Per-pixel classification is not all you need for semantic segmentation,” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
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2021
Later among the works it cites.
2021
Later among the works it cites.
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2019
Cited alongside, same era.
Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “Unet++: Redesigning skip connections to exploit multiscale features in image segmentation,” IEEE transactions on medical imaging , vol. 39, no. 6, pp. 1856–1867, 2019
2019
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N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in European conference on computer vision . Springer, 2020, pp. 213–229
2020
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S. Fu, Y. Lu, Y. Wang, Y. Zhou, W. Shen, E. Fishman, and A. Yuille, “Domain adaptive relational reasoning for 3d multi-organ segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2020, pp. 656–666
2020
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E. Grøvik, D. Yi, M. Iv, E. Tong, D. Rubin, and G. Zaharchuk, “Deep learning enables automatic detection and segmentation of brain metastases on multisequence mri,” Journal of Magnetic Resonance Imaging , vol. 51, no. 1, pp. 175–182, 2020
2020
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X. Luo, J. Chen, T. Song, Y. Chen, G. Wang, and S. Zhang, “Semi-supervised medical image segmentation through dual-task consistency,” AAAI Conference on Artificial Intelligence , 2021
2021
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2021
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A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” in ICLR , 2021
2021
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Y. Xie, J. Zhang, C. Shen, and Y. Xia, “Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part III 24 . Springer, 2021, pp. 171–180
2021
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H. Touvron, M. Cord, A. Sablayrolles, G. Synnaeve, and H. Jégou, “Going deeper with image transformers,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 32–42
2021
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F. Isensee, P. F. Jaeger, S. A. Kohl, J. Petersen, and K. H. Maier-Hein, “nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,” Nature methods , vol. 18, no. 2, pp. 203–211, 2021
2021
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H. Cao, Y. Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, and M. Wang, “Swin-unet: Unet-like pure transformer for medical image segmentation,” in European conference on computer vision . Springer, 2022, pp. 205–218
2022
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B. Cheng, I. Misra, A. G. Schwing, A. Kirillov, and R. Girdhar, “Masked-attention mask transformer for universal image segmentation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 1290–1299
2022
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Q. Yu, H. Wang, S. Qiao, M. Collins, Y. Zhu, H. Adam, A. Yuille, and L.-C. Chen, “k-means mask transformer,” in European Conference on Computer Vision . Springer, 2022, pp. 288–307
2022
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Q. Yu, H. Wang, D. Kim, S. Qiao, M. Collins, Y. Zhu, H. Adam, A. Yuille, and L.-C. Chen, “Cmt-deeplab: Clustering mask transformers for panoptic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 2560–2570
2022
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H. Peiris, M. Hayat, Z. Chen, G. Egan, and M. Harandi, “A robust volumetric transformer for accurate 3d tumor segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2022, pp. 162–172
2022
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H.-Y. Zhou, J. Guo, Y. Zhang, X. Han, L. Yu, L. Wang, and Y. Yu, “nnformer: Volumetric medical image segmentation via a 3d transformer,” IEEE Transactions on Image Processing , 2023
2023
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2023
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E. Oermann, K. Link, Z. Schnurman, C. Liu, Y. J. F. Kwon, L. Y. Jiang, M. Nasir-Moin, S. Neifert, J. Alzate, K. Bernstein et al. , “Longitudinal deep neural networks for assessing metastatic brain cancer on a massive open benchmark.” preprint , 2023
2023
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2023
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