Fetching the paper…
Reading the bibliography…
Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying the progression of the malignant entity.
Louis, D.N., Ohgaki, H., Wiestler, O.D., Cavenee, W.K., Burger, P.C., Jouvet, A., Scheithauer, B.W., Kleihues, P.: The 2007 who classification of tumours of the central nervous system. Acta neuropathologica 114
2007
Earlier work this paper cites.
Zacharaki, E.I., Wang, S., Chawla, S., Soo Yoo, D., Wolf, R., Melhem, E.R., Davatzikos, C.: Classification of brain tumor type and grade using mri texture and shape in a machine learning scheme. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 62
2009
Earlier work this paper cites.
Hoover, J.M., Morris, J.M., Meyer, F.B.: Use of preoperative magnetic resonance imaging t1 and t2 sequences to determine intraoperative meningioma consistency. Surgical neurology international 2
2011
Earlier work this paper cites.
Grover, V.P., Tognarelli, J.M., Crossey, M.M., Cox, I.J., Taylor-Robinson, S.D., McPhail, M.J.: Magnetic resonance imaging: principles and techniques: lessons for clinicians. Journal of clinical and experimental hepatology 5
2015
Earlier work this paper cites.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3431–3440 (2015)
2015
Earlier work this paper cites.
Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E.R., Weber, M.A., Arbel, T., Avants, B.B., Ayache, N., Buendia, P., Collins, D.L., Cordier, N., Corso, J.J., Criminisi, A., Das, T., Delingette, H., Demiralp, C., Durst, C.R., Dojat, M., Doyle, S., Festa, J., Forbes, F., Geremia, E., Glocker, B., Golland, P., Guo, X., Hamamci, A., Iftekharuddin, K.M., Jena, R., John, N.M., Konukoglu, E., Lashkari, D., Mariz, J.A., Meier, R., Pereira, S., Precup, D., Price, S.J., Raviv, T.R., Reza, S.M.S., Ryan, M.T., Sarikaya, D., Schwartz, L.H., Shin, H.C., Shotton, J., Silva, C.A., Sousa, N., Subbanna, N.K., Szekely, G., Taylor, T.J., Thomas, O.M., Tustison, N.J., Unal, G.B., Vasseur, F., Wintermark, M., Ye, D.H., Zhao, L., Zhao, B., Zikic, D., Prastawa, M., Reyes, M., Leemput, K.V.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34
2015
Earlier work this paper cites.
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 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 (2016)
2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Nie, D., Zhang, H., Adeli, E., Liu, L., Shen, D.: 3d deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients. In: International conference on medical image computing and computer-assisted intervention. pp. 212–220. Springer (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., John Freymann, K.F., Davatzikos, C.: Segmentation labels and radiomic features for the pre-operative scans of the tcga-gbm collection. The Cancer Imaging Archive (2017), https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q
2017
Earlier work this paper cites.
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., John Freymann, K.F., Davatzikos, C.: Segmentation labels and radiomic features for the pre-operative scans of the tcga-lgg collection. The Cancer Imaging Archive (2017), https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF
2017
Earlier work this paper cites.
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific data 4
2017
Earlier work this paper cites.
Kamnitsas, K., W. Bai, E.F., McDonagh, S., Sinclair, M., Pawlowski, N., Rajchl, M., Lee, M., Kainz, B., Rueckert, D., Glocker, B.: Ensembles of multiple models and architectures for robust brain tumour segmentation. In: International Conf. on Medical Image Computing and Computer Assisted Intervention. Multimodal Brain Tumor Segmentation Challenge (MICCAI, 2017). LNCS (2017)
2017
Earlier work this paper cites.
Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B.: Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Medical image analysis 36
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems. pp. 5998–6008 (2017)
2017
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Isensee, F., Jäger, P.F., Full, P.M., Vollmuth, P., Maier-Hein, K.H.: nnu-net for brain tumor segmentation. In: International MICCAI Brainlesion Workshop. pp. 118–132. Springer (2020)
2020
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Liu, D., Zhang, H., Zhao, M., Yu, X., Yao, S., Zhou, W.: Brain tumor segmention based on dilated convolution refine networks. In: 2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA). pp. 113–120. IEEE (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Zhou, C., Chen, S., Ding, C., Tao, D.: Learning contextual and attentive information for brain tumor segmentation. In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018). Multimodal Brain Tumor Segmentation Challenge (BraTS 2018). BrainLes 2018 workshop. LNCS, Springer (2018)
2018
Cited alongside, same era.
Chen, C., Liu, X., Ding, M., Zheng, J., Li, J.: 3d dilated multi-fiber network for real-time brain tumor segmentation in mri. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 184–192. Springer (2019)
2019
Cited alongside, same era.
Huo, Y., Xu, Z., Xiong, Y., Aboud, K., Parvathaneni, P., Bao, S., Bermudez, C., Resnick, S.M., Cutting, L.E., Landman, B.A.: 3d whole brain segmentation using spatially localized atlas network tiles. NeuroImage 194
2019
Cited alongside, same era.
Jiang, Z., Ding, C., Liu, M., Tao, D.: Two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task. In: International MICCAI Brainlesion Workshop. pp. 231–241. Springer (2019)
2019
Cited alongside, same era.
Myronenko, A., Hatamizadeh, A.: Robust semantic segmentation of brain tumor regions from 3d mris. In: International MICCAI Brainlesion Workshop. pp. 82–89. Springer (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE/CVF International Conference on Computer Vision (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C., Dosovitskiy, A.: Do vision transformers see like convolutional neural networks? Advances in Neural Information Processing Systems 34
2021
Later among the works it cites.
2021
Later among the works it cites.
Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: Transbts: Multimodal brain tumor segmentation using transformer. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 109–119. Springer (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P.H., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6881–6890 (2021)
2021
Later among the works it cites.