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Convolution is an equivariant operation, and image position does not affect its result.
Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules
Junji Shiraishi, Shigehiko Katsuragawa, Junpei Ikezoe, Tsuneo Matsumoto, Takeshi Kobayashi, Ken-ichi Komatsu, Mitate Matsui, Hiroshi Fujita, Yoshie Kodera, and Kunio Doi · 2000
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Richard Szeliski · 2011
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Renet: A recurrent neural network based alternative to convolutional networks
Francesco Visin, Kyle Kastner, Kyunghyun Cho, Matteo Matteucci, Aaron Courville, and Yoshua Bengio · 2015
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Automatic cardiac disease assessment on cine-mri via time-series segmentation and domain specific features
Fabian Isensee, Paul F Jaeger, Peter M Full, Ivo Wolf, Sandy Engelhardt, and Klaus H Maier-Hein · 2017
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Dynamic routing between capsules
Sara Sabour, Nicholas Frosst, and Geoffrey E Hinton · 2017
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Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?
Olivier Bernard, Alain Lalande, Clement Zotti, Frederick Cervenansky, Xin Yang, Pheng-Ann Heng, Irem Cetin, Karim Lekadir, Oscar Camara, Miguel Angel Gonzalez Ballester, et al · 2018
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Improving the segmentation of anatomical structures in chest radiographs using u-net with an imagenet pre-trained encoder
Maayan Frid-Adar, Avi Ben-Cohen, Rula Amer, and Hayit Greenspan · 2018
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Fully convolutional architectures for multiclass segmentation in chest radiographs
Alexey A Novikov, Dimitrios Lenis, David Major, Jiří Hladůvka, Maria Wimmer, and Katja Bühler · 2018
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Convolutional neural network with shape prior applied to cardiac MRI segmentation
Clement Zotti, Zhiming Luo, Alain Lalande, and Pierre-Marc Jodoin · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas G. Dietterich · 2019
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Anatomical priors for image segmentation via post-processing with denoising autoencoders
Agostina J Larrazabal, Cesar Martinez, and Enzo Ferrante · 2019
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Understanding image representations by measuring their equivariance and equivalence
Karel. Lenc and Andrea Vedaldi · 2019
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Cardiac MRI segmentation with strong anatomical guarantees
Nathan Painchaud, Youssef Skandarani, Thierry Judge, Olivier Bernard, Alain Lalande, and Pierre-Marc Jodoin · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Matwo-capsnet: A multi-label semantic segmentation capsules network
Savinien Bonheur, Darko Štern, Christian Payer, Michael Pienn, Horst Olschewski, and Martin Urschler · 2019
Cited alongside, same era.
Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2019
Cited alongside, same era.
3d left ventricular segmentation from 2d cardiac MR images using spatial context
Sofie Tilborghs, Tom Dresselaers, Piet Claus, Jan Bogaert, and Frederik Maes · 2019
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How much position information do convolutional neural networks encode?
Md Amirul Islam, Sen Jia, and Neil DB Bruce · 2020
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