The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
nnU-Net for Brain Tumor Segmentation
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016) · 2016 Fourth International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.79 · Cited by 11,931
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_49 · Cited by 7,945
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049
Cited by
Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H. R., & Xu, D. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-08999-2_22 · Cited by 2,273
Medical Image Data Augmentation: Techniques, Comparisons and Interpretations
Goceri, E. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10453-z · Cited by 459
U-Net-Based Medical Image Segmentation
Yin, X.-X., Sun, L., Fu, Y., Lu, R., & Zhang, Y. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4189781 · Cited by 318
RetractedSwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation Using Swin Transformer
Jiang, Y., Zhang, Y., Lin, X., Dong, J., Cheng, T., & Liang, J. (2022) · Brain Sciences · doi:10.3390/brainsci12060797 · Cited by 264
Performance of large language models on a neurology board-style examination
Schubert, M. C., Wick, W., & Venkataramani, V. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.46721 · Cited by 115
Metadata
- Authors
- Isensee, F., Jäger, P. F., Full, P. M., Vollmuth, P., & Maier-Hein, K. H.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 118-132
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3097618044
- Cited by
- 441
- References
- 29
- FWCI
- 46.18
- Type
- conference paper
- Language
- en
- Open access
- green
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- Isensee, F., Jäger, P. F., Full, P. M., Vollmuth, P., & Maier-Hein, K. H. (2021). nnU-Net for Brain Tumor Segmentation. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-72087-2_11
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