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Automatic and accurate segmentation of brain MR images throughout the human lifespan into tissue and structure is crucial for understanding brain development and diagnosing diseases.
Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F., Behrens, T.E., Johansen-Berg, H., Bannister, P.R., De Luca, M., Drobnjak, I., Flitney, D.E., et al.: Advances in functional and structural mr image analysis and implementation as fsl. Neuroimage 23
2004
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2006
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2008
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Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22
2009
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Stiles, J., Jernigan, T.L.: The basics of brain development. Neuropsychology review 20
2010
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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
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Fischl, B.: Freesurfer. Neuroimage 62
2012
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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
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González-Villà, S., Oliver, A., Valverde, S., Wang, L., Zwiggelaar, R., Lladó, X.: A review on brain structures segmentation in magnetic resonance imaging. Artificial intelligence in medicine 73
2016
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Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV). pp. 565–571. Ieee (2016)
2016
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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
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Casey, B.J., Cannonier, T., Conley, M.I., Cohen, A.O., Barch, D.M., Heitzeg, M.M., Soules, M.E., Teslovich, T., Dellarco, D.V., Garavan, H., et al.: The adolescent brain cognitive development (abcd) study: imaging acquisition across 21 sites. Developmental cognitive neuroscience 32
2018
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Schaefer, A., Kong, R., Gordon, E.M., Laumann, T.O., Zuo, X.N., Holmes, A.J., Eickhoff, S.B., Yeo, B.T.: Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity mri. Cerebral cortex 28
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 10012–10022 (2021)
2021
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
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2022
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Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision. pp. 574–584 (2022)
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2018
Cited alongside, same era.
Cole, J.H., Marioni, R.E., Harris, S.E., Deary, I.J.: Brain age and other bodily ‘ages’: implications for neuropsychiatry. Molecular psychiatry 24
2019
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Roy, A.G., Conjeti, S., Navab, N., Wachinger, C., Initiative, A.D.N., et al.: Quicknat: A fully convolutional network for quick and accurate segmentation of neuroanatomy. NeuroImage 186
2019
Cited alongside, same era.
Dolz, J., Desrosiers, C., Wang, L., Yuan, J., Shen, D., Ayed, I.B.: Deep cnn ensembles and suggestive annotations for infant brain mri segmentation. Computerized Medical Imaging and Graphics 79
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop. pp. 272–284. Springer (2021)
2021
Cited alongside, same era.
2022
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Jia, M., Tang, L., Chen, B.C., Cardie, C., Belongie, S., Hariharan, B., Lim, S.N.: Visual prompt tuning. In: European Conference on Computer Vision. pp. 709–727. Springer (2022)
2022
Later among the works it cites.
Zhang, S., Ren, B., Yu, Z., Yang, H., Han, X., Chen, X., Zhou, Y., Shen, D., Zhang, X.Y.: Tw-net: Transformer weighted network for neonatal brain mri segmentation. IEEE Journal of Biomedical and Health Informatics 27
2022
Later among the works it cites.
Kang, L., Gong, H., Wan, X., Li, H.: Visual-attribute prompt learning for progressive mild cognitive impairment prediction. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 547–557. Springer (2023)
2023
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2023
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Zhao, Z., Wang, S., Gu, J., Zhu, Y., Mei, L., Zhuang, Z., Cui, Z., Wang, Q., Shen, D.: Chatcad+: Towards a universal and reliable interactive cad using llms. IEEE Transactions on Medical Imaging (2024)
2024
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