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Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning.
C2fnas: Coarse-to-fine neural architecture search for 3d medical image segmentation
Q. Yu, D. Yang, H. Roth, Y. Bai, Y. Zhang, A. L. Yuille, and D. Xu · 1904
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Comparison of different methods for delineation of 18f-fdg pet–positive tissue for target volume definition in radiotherapy of patients with non–small cell lung cancer
U. Nestle, S. Kremp, A. Schaefer-Schuler, C. Sebastian-Welsch, D. Hellwig, C. Rübe, and C.-M. Kirsch · 2005
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Sequential model-based optimization for general algorithm configuration
F. Hutter, H. H. Hoos, and K. Leyton-Brown · 2011
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Random search for hyper-parameter optimization
J. Bergstra and Y. Bengio · 2012
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The medical imaging interaction toolkit: challenges and advances
M. Nolden, S. Zelzer, A. Seitel, D. Wald, M. Müller, A. M. Franz, D. Maleike, M. Fangerau, M. Baumhauer, L. Maier-Hein, et al · 2013
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Rectifier nonlinearities improve neural network acoustic models
A. L. Maas, A. Y. Hannun, and A. Y. Ng · 2013
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Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
H. J. Aerts, E. R. Velazquez, R. T. Leijenaar, C. Parmar, P. Grossmann, S. Carvalho, J. Bussink, R. Monshouwer, B. Haibe-Kains, D. Rietveld, et al · 2014
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The multimodal brain tumor image segmentation benchmark (brats)
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
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Evaluation of prostate segmentation algorithms for mri: the promise12 challenge
G. Litjens, R. Toth, W. van de Ven, C. Hoeks, S. Kerkstra, B. van Ginneken, G. Vincent, G. Guillard, N. Birbeck, J. Zhang, et al · 2014
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The multimodal brain tumor image segmentation benchmark (brats)
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
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge, 2015
B. Landman, Z. Xu, J. Eugenio Igelsias, M. Styner, T. Langerak, and A. Klein · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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V-net: Fully convolutional neural networks for volumetric medical image segmentation
F. Milletari, N. Navab, and S.-A. Ahmadi · 2016
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Instance normalization: The missing ingredient for fast stylization
D. Ulyanov, A. Vedaldi, and V. Lempitsky · 2016
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Bridging category-level and instance-level semantic image segmentation
Z. Wu, C. Shen, and A. v. d. Hengel · 2016
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3d u-net: learning dense volumetric segmentation from sparse annotation
Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger · 2016
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The importance of skip connections in biomedical image segmentation
M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury, and C. Pal · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
Cited alongside, same era.
Instance normalization: The missing ingredient for fast stylization
D. Ulyanov, A. Vedaldi, and V. Lempitsky · 2016
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2017
Cited alongside, same era.
Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
Cited alongside, same era.
The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation
nnu-net: Self-adapting framework for u-net-based medical image segmentation
F. Isensee, J. Petersen, A. Klein, D. Zimmerer, P. F. Jaeger, S. Kohl, J. Wasserthal, G. Koehler, T. Norajitra, S. Wirkert, et al · 2018
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The liver tumor segmentation benchmark (lits)
P. Bilic, P. F. Christ, E. Vorontsov, G. Chlebus, H. Chen, Q. Dou, C.-W. Fu, X. Han, P.-A. Heng, J. Hesser, et al · 2019
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Autoaugment: Learning augmentation strategies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2019
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Neural architecture search: A survey
T. Elsken, J. H. Metzen, and F. Hutter · 2019
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U-net: deep learning for cell counting, detection, and morphometry
T. Falk, D. Mai, R. Bensch, Ö. Çiçek, A. Abdulkadir, Y. Marrakchi, A. Böhm, J. Deubner, Z. Jäckel, K. Seiwald, et al · 2019
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S. Jégou, M. Drozdzal, D. Vazquez, A. Romero, and Y. Bengio · 2017
Cited alongside, same era.
A survey on deep learning in medical image analysis
G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. Van Der Laak, B. Van Ginneken, and C. I. Sánchez · 2017
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2017
Cited alongside, same era.
Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
Cited alongside, same era.
The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation
S. Jégou, M. Drozdzal, D. Vazquez, A. Romero, and Y. Bengio · 2017
Cited alongside, same era.
Longitudinal multiple sclerosis lesion segmentation: resource and challenge
A. Carass, S. Roy, A. Jog, J. L. Cuzzocreo, E. Magrath, A. Gherman, J. Button, J. Nguyen, F. Prados, C. H. Sudre, et al · 2017
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2017
Cited alongside, same era.
N. Heller, F. Isensee, K. H. Maier-Hein, X. Hou, C. Xie, F. Li, Y. Nan, G. Mu, Z. Lin, M. Han, et al · 2019
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Automated quantitative tumour response assessment of mri in neuro-oncology with artificial neural networks: a multicentre, retrospective study
P. Kickingereder, F. Isensee, I. Tursunova, J. Petersen, U. Neuberger, D. Bonekamp, G. Brugnara, M. Schell, T. Kessler, M. Foltyn, et al · 2019
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1.1 deep learning hardware: Past, present, and future
Y. LeCun · 2019
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A. L. Simpson, M. Antonelli, S. Bakas, M. Bilello, K. Farahani, B. van Ginneken, A. Kopp-Schneider, B. A. Landman, G. Litjens, B. Menze, et al · 2019
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Methods and open-source toolkit for analyzing and visualizing challenge results
M. Wiesenfarth, A. Reinke, B. A. Landman, M. J. Cardoso, L. Maier-Hein, and A. Kopp-Schneider · 2019
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The liver tumor segmentation benchmark (lits)
P. Bilic, P. F. Christ, E. Vorontsov, G. Chlebus, H. Chen, Q. Dou, C.-W. Fu, X. Han, P.-A. Heng, J. Hesser, et al · 2019
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N. Heller, F. Isensee, K. H. Maier-Hein, X. Hou, C. Xie, F. Li, Y. Nan, G. Mu, Z. Lin, M. Han, et al · 2019
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Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al · 2019
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S. Singh and S. Krishnan · 2019
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The liver tumor segmentation benchmark (lits)
P. Bilic, P. F. Christ, E. Vorontsov, G. Chlebus, H. Chen, Q. Dou, C.-W. Fu, X. Han, P.-A. Heng, J. Hesser, et al · 2019
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The liver tumor segmentation benchmark (lits)
P. Bilic, P. F. Christ, E. Vorontsov, G. Chlebus, H. Chen, Q. Dou, C.-W. Fu, X. Han, P.-A. Heng, J. Hesser, et al · 2019
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N. Heller, F. Isensee, K. H. Maier-Hein, X. Hou, C. Xie, F. Li, Y. Nan, G. Mu, Z. Lin, M. Han, et al · 2019
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N. Heller, N. Sathianathen, A. Kalapara, E. Walczak, K. Moore, H. Kaluzniak, J. Rosenberg, P. Blake, Z. Rengel, M. Oestreich, et al · 2019
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An attempt at beating the 3d u-net
F. Isensee and K. H. Maier-Hein · 2019
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A. L. Simpson, M. Antonelli, S. Bakas, M. Bilello, K. Farahani, B. van Ginneken, A. Kopp-Schneider, B. A. Landman, G. Litjens, B. Menze, et al · 2019
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Multiorgan segmentation using distance-aware adversarial networks
R. Trullo, C. Petitjean, B. Dubray, and S. Ruan · 2019
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Near real-time intraoperative brain tumor diagnosis using stimulated raman histology and deep neural networks
T. C. Hollon, B. Pandian, A. R. Adapa, E. Urias, A. V. Save, S. S. S. Khalsa, D. G. Eichberg, R. S. D’Amico, Z. U. Farooq, S. Lewis, et al · 2020
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batchgenerators - a python framework for data augmentation, Jan. 2020
I. Fabian, J. Paul, W. Jakob, Z. David, P. Jens, K. Simon, S. Justus, K. Andre, R. Tobias, W. Sebastian, N. Peter, D. Stefan, K. Gregor, and M.-H. Klaus · 2020
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Chaos challenge–combined (ct-mr) healthy abdominal organ segmentation
A. E. Kavur, N. S. Gezer, M. Barış, P.-H. Conze, V. Groza, D. D. Pham, S. Chatterjee, P. Ernst, S. Özkan, B. Baydar, et al · 2020
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