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Medical images such as 3D computerized tomography (CT) scans and pathology images, have hundreds of millions or billions of voxels/pixels.
Large scale distributed deep networks
J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, M. Mao, A. Senior, P. Tucker, K. Yang, Q. V. Le, et al · 2012
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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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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Patch-based convolutional neural network for whole slide tissue image classification
L. Hou, D. Samaras, T. M. Kurc, Y. Gao, J. E. Davis, and J. H. Saltz · 2016
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The design and evaluation of interfaces for navigating gigapixel images in digital pathology
R. A. Ruddle, R. G. Thomas, R. Randell, P. Quirke, and D. Treanor · 2016
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Dcan: Deep contour-aware networks for object instance segmentation from histology images
H. Chen, X. Qi, L. Yu, Q. Dou, J. Qin, and P.-A. Heng · 2017
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Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
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Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations
C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. J. Cardoso · 2017
Cited alongside, same era.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Cited alongside, same era.
Deep learning based automatic liver tumor segmentation in ct with shape-based post-processing
G. Chlebus, A. Schenk, J. H. Moltz, B. van Ginneken, H. K. Hahn, and H. Meine · 2018
Cited alongside, same era.
Beyond data and model parallelism for deep neural networks
Z. Jia, M. Zaharia, and A. Aiken · 2018
Cited alongside, same era.
H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes
X. Li, H. Chen, X. Qi, Q. Dou, C.-W. Fu, and P.-A. Heng · 2018
Cited alongside, same era.
Mesh-tensorflow: Deep learning for supercomputers
N. Shazeer, Y. Cheng, N. Parmar, D. Tran, A. Vaswani, P. Koanantakool, P. Hawkins, H. Lee, M. Hong, C. Young, et al · 2018
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Adafactor: Adaptive learning rates with sublinear memory cost
N. Shazeer and M. Stern · 2018
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Liver lesion segmentation informed by joint liver segmentation
E. Vorontsov, A. Tang, C. Pal, and S. Kadoury · 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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A smart system of 3d liver tumour segmentation
A. Biswas, P. Bhattacharya, and S. P. Maity · 2019
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Predicting cancer outcomes from histology and genomics using convolutional networks
P. Mobadersany, S. Yousefi, M. Amgad, D. A. Gutman, J. S. Barnholtz-Sloan, J. E. V. Vega, D. J. Brat, and L. A. Cooper · 2018
Cited alongside, same era.
N. Dryden, N. Maruyama, T. Benson, T. Moon, M. Snir, and B. Van Essen · 2019
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