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This manuscript describes the first challenge on Federated Learning, namely the Federated Tumor Segmentation (FeTS) challenge 2021.
S. K. Warfield, K. H. Zou, and W. M. Wells, “Validation of image segmentation and expert quality with an expectation-maximization algorithm,” pp. 298–306, 2002
2002
Earlier work this paper cites.
G. J. Annas et al
2003
Earlier work this paper cites.
Springer Science & Business Media, 2005
R. T. Rockafellar and R. J.-B. Wets, Variational analysis · 2005
Earlier work this paper cites.
H. R. Roth, K. Chang, P. Singh, N. Neumark, W. Li, V. Gupta, S. Gupta, L. Qu, A. Ihsani, B. C. Bizzo, Y. Wen, V. Buch, M. Shah, F. Kitamura, M. Mendonça, V. Lavor, A. Harouni, C. Compas, J. Tetreault, P. Dogra, Y. Cheng, S. Erdal, R. White, B. Hashemian, T. Schultz, M. Zhang, A. McCarthy, B. M. Yun, E. Sharaf, K. V. Hoebel, J. B. Patel, B. Chen, S. Ko, E. Leibovitz, E. D. Pisano, L. Coombs, D. Xu, K. J. Dreyer, I. Dayan, R. C. Naidu, M. Flores, D. Rubin, and J. Kalpathy-Cramer, “Federated Learning for Breast Density Classification: A Real-World Implementation,” arXiv:2009.01871 [cs, eess] · 2009
Earlier work this paper cites.
P. Wang, C. Shen, H. R. Roth, D. Yang, D. Xu, M. Oda, K. Misawa, P.-T. Chen, K.-L. Liu, W.-C. Liao, W. Wang, and K. Mori, “Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning,” arXiv:2009.13148 [cs, eess] · 2009
Earlier work this paper cites.
T. Rohlfing, N. M. Zahr, E. V. Sullivan, and A. Pfefferbaum, “The sri24 multichannel atlas of normal adult human brain structure,” Human brain mapping
2010
Earlier work this paper cites.
G. Blanchard, G. Lee, and C. Scott, “Generalizing from several related classification tasks to a new unlabeled sample,” Advances in neural information processing systems
2011
Earlier work this paper cites.
B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest, et al
2014
Earlier work this paper cites.
Q. T. Ostrom, H. Gittleman, J. Fulop, M. Liu, R. Blanda, C. Kromer, Y. Wolinsky, C. Kruchko, and J. S. Barnholtz-Sloan, “Cbtrus statistical report: primary brain and central nervous system tumors diagnosed in the united states in 2008-2012,” Neuro-oncology
2015
Earlier work this paper cites.
S. Bakas, K. Zeng, A. Sotiras, S. Rathore, H. Akbari, B. Gaonkar, M. Rozycki, S. Pati, and C. Davatzikos, “Glistrboost: combining multimodal mri segmentation, registration, and biophysical tumor growth modeling with gradient boosting machines for glioma segmentation,” in BrainLes 2015
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention
2015
Earlier work this paper cites.
K. Zeng, S. Bakas, A. Sotiras, H. Akbari, M. Rozycki, S. Rathore, S. Pati, and C. Davatzikos, “Segmentation of gliomas in pre-operative and post-operative multimodal magnetic resonance imaging volumes based on a hybrid generative-discriminative framework,” in International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
2016
Earlier work this paper cites.
P. A. Yushkevich, J. Pluta, H. Wang, L. E. Wisse, S. Das, and D. Wolk, “Fast automatic segmentation of hippocampal subfields and medial temporal lobe subregions in 3 tesla and 7 tesla t2-weighted mri,” Alzheimer’s & Dementia
2016
Earlier work this paper cites.
M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury, and C. Pal, “The importance of skip connections in biomedical image segmentation,” in Deep Learning and Data Labeling for Medical Applications
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2016
Earlier work this paper cites.
Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3d u-net: learning dense volumetric segmentation from sparse annotation,” in International conference on medical image computing and computer-assisted intervention
2016
Earlier work this paper cites.
S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. S. Kirby, J. B. Freymann, K. Farahani, and C. Davatzikos, “Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features,” Scientific data
2017
Earlier work this paper cites.
K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, and B. Glocker, “Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation,” Medical image analysis
2017
Earlier work this paper cites.
P. Voigt and A. Von dem Bussche, “The eu general data protection regulation (gdpr),” A Practical Guide, 1st Ed., Cham: Springer International Publishing
2017
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial Intelligence and Statistics
2017
Cited alongside, same era.
S. Bakas, H. Akbari, A. Sotiras, et al
2017
Cited alongside, same era.
S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. Kirby, J. Freymann, K. Farahani, and C. Davatzikos, “Segmentation labels and radiomic features for the pre-operative scans of the tcga-lgg collection,” The cancer imaging archive
2017
Cited alongside, same era.
S. Rathore, S. Bakas, S. Pati, H. Akbari, R. Kalarot, P. Sridharan, M. Rozycki, M. Bergman, B. Tunc, R. Verma, et al
2017
Cited alongside, same era.
G. M. Kurtzer, V. Sochat, and M. W. Bauer, “Singularity: Scientific containers for mobility of compute,” PloS one
2017
Cited alongside, same era.
S. Pati, R. Verma, H. Akbari, M. Bilello, V. B. Hill, C. Sako, R. Correa, N. Beig, L. Venet, S. Thakur, et al
2020
Later among the works it cites.
N. Rieke, J. Hancox, W. Li, F. Milletari, H. R. Roth, S. Albarqouni, S. Bakas, M. N. Galtier, B. A. Landman, K. Maier-Hein, et al
2020
Later among the works it cites.
M. J. Sheller, B. Edwards, G. A. Reina, J. Martin, S. Pati, A. Kotrotsou, M. Milchenko, W. Xu, D. Marcus, R. R. Colen, et al
2020
Later among the works it cites.
L. Maier-Hein, A. Reinke, M. Kozubek, A. L. Martel, T. Arbel, M. Eisenmann, A. Hanbury, P. Jannin, H. Müller, S. Onogur, et al
2020
Later among the works it cites.
S. Thakur, J. Doshi, S. Pati, S. Rathore, C. Sako, M. Bilello, S. M. Ha, G. Shukla, A. Flanders, A. Kotrotsou, et al
2020
Later among the works it cites.
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2018
Cited alongside, same era.
2018
Cited alongside, same era.
R. McKinley, R. Meier, and R. Wiest, “Ensembles of densely-connected cnns with label-uncertainty for brain tumor segmentation,” in International MICCAI Brainlesion Workshop
2018
Cited alongside, same era.
2018
Cited alongside, same era.
C. Davatzikos, S. Rathore, S. Bakas, S. Pati, M. Bergman, R. Kalarot, P. Sridharan, A. Gastounioti, N. Jahani, E. Cohen, et al
2018
Cited alongside, same era.
H. Iqbal, “Harisiqbal88/plotneuralnet v1.0.0,” Dec. 2018
2018
Cited alongside, same era.
M. J. Sheller, G. A. Reina, B. Edwards, J. Martin, and S. Bakas, “Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation,” in International MICCAI Brainlesion Workshop
2018
Cited alongside, same era.
M. Nagendran, Y. Chen, C. A. Lovejoy, A. C. Gordon, M. Komorowski, H. Harvey, E. J. Topol, J. P. Ioannidis, G. S. Collins, and M. Maruthappu, “Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies,” bmj
2020
Later among the works it cites.
E. Beede, E. Baylor, F. Hersch, A. Iurchenko, L. Wilcox, P. Ruamviboonsuk, and L. M. Vardoulakis, “A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy,” in Proceedings of the 2020 CHI conference on human factors in computing systems
2020
Later among the works it cites.
L. Zhang, X. Wang, D. Yang, T. Sanford, S. Harmon, B. Turkbey, B. J. Wood, H. Roth, A. Myronenko, D. Xu, et al
2020
Later among the works it cites.
2020
Later among the works it cites.
Q. Liu, Q. Dou, and P.-A. Heng, “Shape-aware meta-learning for generalizing prostate mri segmentation to unseen domains,” in International Conference on Medical Image Computing and Computer-Assisted Intervention
2020
Later among the works it cites.
_eprint: https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/mp.13880
S. W. Remedios, S. Roy, C. Bermudez, M. B. Patel, J. A. Butman, B. A. Landman, and D. L. Pham, “Distributed deep learning across multisite datasets for generalized CT hemorrhage segmentation,” Medical Physics · 2020
Later among the works it cites.
doi: 10.5281/zenodo.4573128
S. Bakas, M. Sheller, S. Pati, B. Edwards, G. A. Reina, U. Baid, Y. Chen, R. T. Shinohara, J. Martin, B. Menze, S. Albarqouni, M. Bilello, S. Mohan, J. B. Freymann, J. S. Kirb, C. Davatzikos, H. Fathallah-Shaykh, R. Wiest, A. Jakab, R. R. Colen, A. Kotrotsou, D. Marcus, M. Milchenko, A. Nazeri, M.-A. Weber, A. Mahajan, U. Baid, and P. Vollmuth, “Federated tumor segmentation,” Mar. 2021 · 2021
Closest in time.
S. Pati, “Fets-ai/labelfusion: Sdist added to pypi,” Mar. 2021
2021
Closest in time.
2021
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I. Gulrajani and D. Lopez-Paz, “In search of lost domain generalization,” in International Conference on Learning Representations
2021
Closest in time.
N. Karani, E. Erdil, K. Chaitanya, and E. Konukoglu, “Test-time adaptable neural networks for robust medical image segmentation,” Medical Image Analysis
2021
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2021
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K. V. Sarma, S. Harmon, T. Sanford, H. R. Roth, Z. Xu, J. Tetreault, D. Xu, M. G. Flores, A. G. Raman, R. Kulkarni, B. J. Wood, P. L. Choyke, A. M. Priester, L. S. Marks, S. S. Raman, D. Enzmann, B. Turkbey, W. Speier, and C. W. Arnold, “Federated learning improves site performance in multicenter deep learning without data sharing,” Journal of the American Medical Informatics Association
2021
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