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Convolutional Neural Networks (CNNs) are the predominant model used for a variety of medical image analysis tasks.
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V-net: Fully convolutional neural networks for volumetric medical image segmentation
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Xnor-net: Imagenet classification using binary convolutional neural networks
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Deep learning , volume 1
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Densely connected convolutional networks
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Deep learning for segmentation using an open large-scale dataset in 2d echocardiography
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C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. Jorge Cardoso · 2017
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Aggregated residual transformations for deep neural networks
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Designing energy-efficient convolutional neural networks using energy-aware pruning
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Encoder-decoder with atrous separable convolution for semantic image segmentation
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam · 2018
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M. MacKay, P. Vicol, J. Lorraine, D. Duvenaud, and R. Grosse · 2019
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Use-net: Incorporating squeeze-and-excitation blocks into u-net for prostate zonal segmentation of multi-institutional mri datasets
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Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion
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Efficientnet: Rethinking model scaling for convolutional neural networks
M. Tan and Q. V. Le · 2019
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Continual learning with hypernetworks
J. von Oswald, C. Henning, J. Sacramento, and B. F. Grewe · 2019
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A learning strategy for contrast-agnostic mri segmentation
B. Billot, D. N. Greve, K. Van Leemput, B. Fischl, J. E. Iglesias, and A. Dalca · 2020
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What is the state of neural network pruning?
D. Blalock, J. J. G. Ortiz, J. Frankle, and J. Guttag · 2020
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You only train once: Loss-conditional training of deep networks
A. Dosovitskiy and J. Djolonga · 2020
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A survey of the recent architectures of deep convolutional neural networks
A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi · 2020
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Shape adaptor: A learnable resizing module
S. Liu, Z. Lin, Y. Wang, J. Zhang, F. Perazzi, and E. Johns · 2020
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U-net and its variants for medical image segmentation: theory and applications
N. Siddique, P. Sidike, C. Elkin, and V. Devabhaktuni · 2020
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Meta-learning via hypernetworks
D. Zhao, J. von Oswald, S. Kobayashi, J. Sacramento, and B. F. Grewe · 2020
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Squeeze-and-attention networks for semantic segmentation
Z. Zhong, Z. Q. Lin, R. Bidart, X. Hu, I. B. Daya, Z. Li, W.-S. Zheng, J. Li, and A. Wong · 2020
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Editing factual knowledge in language models
N. De Cao, W. Aziz, and I. Titov · 2021
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Learning to downsample for segmentation of ultra-high resolution images
C. Jin, R. Tanno, T. Mertzanidou, E. Panagiotaki, and D. C. Alexander · 2021
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Hyperrecon: Regularization-agnostic cs-mri reconstruction with hypernetworks
A. Q. Wang, A. V. Dalca, and M. R. Sabuncu · 2021
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Learning the effect of registration hyperparameters with hypermorph
A. Hoopes, M. Hoffman, D. N. Greve, B. Fischl, J. Guttag, and A. V. Dalca · 2022
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Learning strides in convolutional neural networks
R. Riad, O. Teboul, D. Grangier, and N. Zeghidour · 2022
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