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Computed tomography (CT) is extensively used for accurate visualization and segmentation of organs and lesions.
O. Ronneberger, P. Fischer, and T. Brox, “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 . Springer, 2015, pp. 234–241
2015
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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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W. Ji, S. Yu, J. Wu, K. Ma, C. Bian, Q. Bi, J. Li, H. Liu, L. Cheng, and Y. Zheng, “Learning calibrated medical image segmentation via multi-rater agreement modeling,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 12 341–12 351
2021
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A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
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S.-C. Huang, L. Shen, M. P. Lungren, and S. Yeung, “Gloria: A multimodal global-local representation learning framework for label-efficient medical image recognition,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 3942–3951
2021
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Z. Xue, P. Li, L. Zhang, X. Lu, G. Zhu, P. Shen, S. A. A. Shah, and M. Bennamoun, “Multi-modal co-learning for liver lesion segmentation on pet-ct images,” IEEE Transactions on Medical Imaging , vol. 40, no. 12, pp. 3531–3542, 2021
2021
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C. Chen, K. Zhou, M. Zha, X. Qu, X. Guo, H. Chen, Z. Wang, and R. Xiao, “An effective deep neural network for lung lesions segmentation from covid-19 ct images,” IEEE Transactions on Industrial Informatics , vol. 17, no. 9, pp. 6528–6538, 2021
2021
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Y. Tang, D. Yang, W. Li, H. R. Roth, B. Landman, D. Xu, V. Nath, and A. Hatamizadeh, “Self-supervised pre-training of swin transformers for 3d medical image analysis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 20 730–20 740
2022
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H. H. Lee, S. Bao, Y. Huo, and B. A. Landman, “3d ux-net: A large kernel volumetric convnet modernizing hierarchical transformer for medical image segmentation,” in The Eleventh International Conference on Learning Representations , 2022
2022
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X. Zhao, P. Zhang, F. Song, C. Ma, G. Fan, Y. Sun, Y. Feng, and G. Zhang, “Prior attention network for multi-lesion segmentation in medical images,” IEEE Transactions on Medical Imaging , vol. 41, no. 12, pp. 3812–3823, 2022
2022
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Z. Wang, Z. Wu, D. Agarwal, and J. Sun, “Medclip: Contrastive learning from unpaired medical images and text,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , 2022, pp. 3876–3887
2022
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Y. Xie, J. Zhang, Y. Xia, and Q. Wu, “Unimiss: Universal medical self-supervised learning via breaking dimensionality barrier,” in European Conference on Computer Vision . Springer, 2022, pp. 558–575
2022
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G. Ghiasi, X. Gu, Y. Cui, and T.-Y. Lin, “Scaling open-vocabulary image segmentation with image-level labels,” in European Conference on Computer Vision . Springer, 2022, pp. 540–557
2022
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Z. Wang, Y. Lu, Q. Li, X. Tao, Y. Guo, M. Gong, and T. Liu, “Cris: Clip-driven referring image segmentation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 11 686–11 695
2022
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P. Müller, G. Kaissis, C. Zou, and D. Rueckert, “Radiological reports improve pre-training for localized imaging tasks on chest x-rays,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2022, pp. 647–657
2022
Cited alongside, same era.
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
2022
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S. Roy, G. Koehler, C. Ulrich, M. Baumgartner, J. Petersen, F. Isensee, P. F. Jaeger, and K. H. Maier-Hein, “Mednext: transformer-driven scaling of convnets for medical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2023, pp. 405–415
2023
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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
S. Pan, C.-W. Chang, T. Wang, J. Wynne, M. Hu, Y. Lei, T. Liu, P. Patel, J. Roper, and X. Yang, “Abdomen ct multi-organ segmentation using token-based mlp-mixer,” Medical Physics , vol. 50, no. 5, pp. 3027–3038, 2023
2023
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2023
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2023
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F. Liang, B. Wu, X. Dai, K. Li, Y. Zhao, H. Zhang, P. Zhang, P. Vajda, and D. Marculescu, “Open-vocabulary semantic segmentation with mask-adapted clip,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 7061–7070
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2023
Cited alongside, same era.
A. Marcus, P. Bentley, and D. Rueckert, “Concurrent ischemic lesion age estimation and segmentation of ct brain using a transformer-based network,” IEEE Transactions on Medical Imaging , vol. 42, no. 12, pp. 3464–3473, 2023
2023
Cited alongside, same era.
M. Moor, O. Banerjee, Z. S. H. Abad, H. M. Krumholz, J. Leskovec, E. J. Topol, and P. Rajpurkar, “Foundation models for generalist medical artificial intelligence,” Nature , vol. 616, no. 7956, pp. 259–265, 2023
2023
Cited alongside, same era.
J. Silva-Rodríguez, J. Dolz, and I. B. Ayed, “Towards foundation models and few-shot parameter-efficient fine-tuning for volumetric organ segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2023, pp. 213–224
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo et al. , “Segment anything,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 4015–4026
2023
Cited alongside, same era.
J. Liu, Y. Zhang, J.-N. Chen, J. Xiao, Y. Lu, B. A Landman, Y. Yuan, A. Yuille, Y. Tang, and Z. Zhou, “Clip-driven universal model for organ segmentation and tumor detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 21 152–21 164
2023
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2023
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X. Zhai, B. Mustafa, A. Kolesnikov, and L. Beyer, “Sigmoid loss for language image pre-training,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 11 975–11 986
2023
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2023
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J. Wasserthal, H.-C. Breit, M. T. Meyer, M. Pradella, D. Hinck, A. W. Sauter, T. Heye, D. T. Boll, J. Cyriac, S. Yang et al. , “Totalsegmentator: robust segmentation of 104 anatomic structures in ct images,” Radiology: Artificial Intelligence , vol. 5, no. 5, 2023
2023
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2023
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I. E. Hamamci, S. Er, F. Almas, A. G. Simsek, S. N. Esirgun, I. Dogan, M. F. Dasdelen, O. F. Durugol, B. Wittmann, T. Amiranashvili et al. , “Developing generalist foundation models from a multimodal dataset for 3d computed tomography,” 2024
2024
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Y. Huang, X. Yang, L. Liu, H. Zhou, A. Chang, X. Zhou, R. Chen, J. Yu, J. Chen, C. Chen et al. , “Segment anything model for medical images?” Medical Image Analysis , vol. 92, p. 103061, 2024
2024
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T. Koleilat, H. Asgariandehkordi, H. Rivaz, and Y. Xiao, “Medclip-sam: Bridging text and image towards universal medical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2024, pp. 643–653
2024
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J. Ma, Y. He, F. Li, L. Han, C. You, and B. Wang, “Segment anything in medical images,” Nature Communications , vol. 15, no. 1, p. 654, 2024
2024
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F. Remy, K. Demuynck, and T. Demeester, “Biolord-2023: semantic textual representations fusing large language models and clinical knowledge graph insights,” Journal of the American Medical Informatics Association , p. ocae029, 2024
2024
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