Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH (2021) nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18(2):203–211

Article  CAS  PubMed  Google Scholar 

Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth HR, Xu D (2022) Unetr: transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp 574–584

Roy AG, Navab N, Wachinger C (2018) Concurrent spatial and channel ‘squeeze and excitation’ in fully convolutional networks. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 421–429

Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2020) An image is worth 16\(\times \)16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems, 30

Jiang Y, Zhang Y, Lin X, Dong J, Cheng T, Liang J (2022) Swinbts: a method for 3d multimodal brain tumor segmentation using swin transformer. Brain Sci 12(6):797

Article  PubMed  PubMed Central  Google Scholar 

Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 10012–10022

Zhou J, Wang P, Wang F, Liu Q, Li H, Jin R (2021) Elsa: enhanced local self-attention for vision transformer. arXiv preprint arXiv:2112.12786

Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 234–241. Springer

Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3d u-net: learning dense volumetric segmentation from sparse annotation. In: International conference on medical image computing and computer-assisted intervention, pp 424–432. Springer

Milletari F, Navab N, Ahmadi S-A (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth international conference on 3D vision (3DV), pp 565–571. IEEE

Isensee F, Petersen J, Klein A, Zimmerer D, Jaeger PF, Kohl S, Wasserthal J, Köhler G, Norajitra T, Wirkert SJ, Maier-Hein KH (2018) nnu-net: Self-adapting framework for u-net-based medical image segmentation. arXiv preprint arXiv:1809.10486

Zhou T, Noeuveglise A, Ghazouani F, Modzelewski R, Thureau S, Fontanilles M, Ruan S (2022) Prediction of brain tumor recurrence location based on Kullback–Leibler divergence and nonlinear correlation learning. In: 2022 26th International conference on pattern recognition (ICPR), pp 4414–4419. IEEE

Zhou T, Ruan S, Vera P, Canu S (2022) A tri-attention fusion guided multi-modal segmentation network. Pattern Recognit 124:108417

Article  Google Scholar 

Li J, Wang W, Chen C, Zhang T, Zha S, Yu H, Wang J (2022) Transbtsv2: wider instead of deeper transformer for medical image segmentation. arXiv preprint arXiv:2201.12785

Wang W, Chen C, Ding M, Yu H, Zha S, Li J (2021) Transbts: multimodal brain tumor segmentation using transformer. In: International conference on medical image computing and computer-assisted intervention, pp 109–119. Springer

Jia Q, Shu H (2022) Bitr-unet: a cnn-transformer combined network for MRI brain tumor segmentation. In: International MICCAI Brainlesion workshop, pp 3–14. Springer

Hatamizadeh A, Nath V, Tang Y, Yang D, Roth HR, Xu D (2022) Swin unetr: swin transformers for semantic segmentation of brain tumors in MRI images. In: International MICCAI brainlesion workshop, pp 272–284. Springer

Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141

Baid U, Ghodasara S, Bilello M, Mohan S, Calabrese E, Colak E, Farahani K, Kalpathy-Cramer J, Kitamura FC, Pati S, Prevedello LM, Rudie JD, Sako C, Shinohara RT, Bergquist T, Chai R, Eddy JA, Elliott J, Reade W, Schaffter T, Yu T, Zheng J, Annotators B, Davatzikos C, Mongan J, Hess C, Cha S, Villanueva-Meyer JE, Freymann JB, Kirby JS, Wiestler B, Crivellaro P, Colen RR, Kotrotsou A, Marcus DS, Milchenko M, Nazeri A, Fathallah-Shaykh HM, Wiest R, Jakab A, Weber M, Mahajan A, Menze BH, Flanders AE, Bakas S (2021) The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314

Luu HM, Park S-H (2021) Extending nn-unet for brain tumor segmentation. In: International MICCAI brainlesion workshop, pp 173–186. Springer

Yuan Y (2021) Evaluating scale attention network for automatic brain tumor segmentation with large multi-parametric MRI database. In: International MICCAI Brainlesion workshop, pp 42–53. Springer

Futrega M, Milesi A, Marcinkiewicz M, Ribalta P (2021) Optimized u-net for brain tumor segmentation. In: International MICCAI brainlesion workshop, pp 15–29. Springer