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The combination of the U-Net based deep learning models and Transformer is a new trend for medical image segmentation.
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T. Xiang, C. Zhang, D. Liu, Y. Song, H. Huang, and W. Cai, “BiO-Net: learning recurrent bi-directional connections for encoder-decoder architecture,” in International conference on medical image computing and computer-assisted intervention . Springer, 2020, pp. 74–84
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M. Antonelli, A. Reinke, S. Bakas, K. Farahani, A. Kopp-Schneider, B. A. Landman, G. Litjens, B. Menze, O. Ronneberger, R. M. Summers et al. , “The medical segmentation decathlon,” Nature communications , vol. 13, no. 1, p. 4128, 2022
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H. Wang, P. Cao, J. Wang, and O. R. Zaiane, “Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer,” in Proceedings of the AAAI conference on artificial intelligence , vol. 36, no. 3, 2022, pp. 2441–2449
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K. Wickstrøm, M. Kampffmeyer, and R. Jenssen, “Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps,” Medical image analysis , vol. 60, p. 101619, 2020
2020
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T. Nair, D. Precup, D. L. Arnold, and T. Arbel, “Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation,” Medical image analysis , vol. 59, p. 101557, 2020
2020
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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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2021
Cited alongside, same era.
J. M. J. Valanarasu, P. Oza, I. Hacihaliloglu, and V. M. Patel, “Medical transformer: Gated axial-attention for medical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2021, pp. 36–46
2021
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S. He, P. E. Grant, and Y. Ou, “Global-local transformer for brain age estimation,” IEEE transactions on medical imaging , vol. 41, no. 1, pp. 213–224, 2021
2021
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S. Budd, E. C. Robinson, and B. Kainz, “A survey on active learning and human-in-the-loop deep learning for medical image analysis,” Medical Image Analysis , vol. 71, p. 102062, 2021
2021
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Y. Shi, J. Zhang, T. Ling, J. Lu, Y. Zheng, Q. Yu, L. Qi, and Y. Gao, “Inconsistency-aware uncertainty estimation for semi-supervised medical image segmentation,” IEEE transactions on medical imaging , vol. 41, no. 3, pp. 608–620, 2021
2021
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2022
Later among the works it cites.
2022
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K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 000–16 009
2022
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W. Choi, N. Dahiya, and S. Nadeem, “CIRDataset: A large-scale dataset for clinically-interpretable lung nodule radiomics and malignancy prediction,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2022, pp. 13–22
2022
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Z. Han, M. Jian, and G.-G. Wang, “ConvUNeXt: An efficient convolution neural network for medical image segmentation,” Knowledge-Based Systems , vol. 253, p. 109512, 2022
2022
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2022
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C. Agarwal, D. D’souza, and S. Hooker, “Estimating example difficulty using variance of gradients,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 10 368–10 378
2022
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S. He, Y. Feng, P. E. Grant, and Y. Ou, “Segmentation ability map: Interpret deep features for medical image segmentation,” Medical Image Analysis , vol. 84, p. 102726, 2023
2023
Closest in time.
K. Donggyun, K. Jinwoo, C. Seongwoong, L. Chong, and H. Seunghoon, “Universal few-shot learning of dense prediction tasks with visual token matching,” https://openreview.net/forum?id=88nT0j5jAn , 2023
2023
Closest in time.