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What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years.
Deformable image registration using a cue-aware deep regression network,
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N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, · 1958
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P. K. Saha, R. Strand, G. Borgefors, · 1964
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E. R. McVeigh, R. M. Henkelman, M. J. Bronskill, · 1985
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Multispectral analysis of magnetic resonance images.,
M. W. Vannier, R. L. Butterfield, D. Jordan, W. A. Murphy, R. G. Levitt, M. Gado, · 1985
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D. E. Rumelhart, G. E. Hinton, R. J. Williams, · 1986
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Automatic recognition of normal and pathological tissue types in MR images,
A. Lundervold, K. Moen, T. Taxt, · 1988
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Approximation by superpositions of a sigmoidal function,
G. Cybenko, · 1989
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Multilayer feedforward networks are universal approximators,
K. Hornik, M. Stinchcombe, H. White, · 1989
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Multispectral analysis of uterine corpus tumors in magnetic resonance imaging.,
T. Taxt, A. Lundervold, B. Fuglaas, H. Lien, V. Abeler, · 1992
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A practical Bayesian framework for backpropagation networks,
D. J. MacKay, · 1992
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Multilayer feedforward networks with a nonpolynomial activation function can approximate any function,
M. Leshno, V. Y. Lin, A. Pinkus, S. Schocken, · 1993
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Automatic lung nodule detection using profile matching and back-propagation neural network techniques.,
S. C. Lo, M. T. Freedman, J. S. Lin, S. K. Mun, · 1993
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Super-resolution methods in mri: can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time?,
E. Plenge, D. H. J. Poot, M. Bernsen, G. Kotek, G. Houston, P. Wielopolski, L. van der Weerd, W. J. Niessen, E. Meijering, · 1993
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J. M. F. Calvin R. Maurer, Jr., A review of medical image registration, 1993
1993
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Multispectral analysis of the brain using magnetic resonance imaging.,
T. Taxt, A. Lundervold, · 1994
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Segmentation of brain parenchyma and cerebrospinal fluid in multispectral magnetic resonance images,
A. Lundervold, G. Storvik, · 1995
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R. M. Neal, Bayesian learning for neural networks, Ph.D. thesis, University of Toronto, 1995
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The Helmholtz machine,
P. Dayan, G. E. Hinton, R. M. Neal, R. S. Zemel, · 1995
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Gradient-based learning applied to document recognition,
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, · 1998
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Estimation of the noise in magnitude MR images.,
J. Sijbers, A. J. den Dekker, J. Van Audekerke, M. Verhoye, D. Van Dyck, · 1998
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A survey of medical image registration.,
J. B. Maintz, M. A. Viergever, · 1998
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Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time,
M. Lysaker, A. Lundervold, X.-C. Tai, · 2003
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Compressed sensing,
D. L. Donoho, · 2006
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Sparse mri: The application of compressed sensing for rapid mr imaging.,
M. Lustig, D. Donoho, J. M. Pauly, · 2007
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Wspm: wavelet-based statistical parametric mapping.,
D. Van De Ville, M. L. Seghier, F. Lazeyras, T. Blu, M. Unser, · 2007
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Calculation of susceptibility through multiple orientation sampling (COSMOS): a method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI,
T. Liu, P. Spincemaille, L. De Rochefort, B. Kressler, Y. Wang, · 2009
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A super-resolution framework for 3-d high-resolution and high-contrast imaging using 2-d multislice mri.,
R. Z. Shilling, T. Q. Robbie, T. Bailloeul, K. Mewes, R. M. Mersereau, M. E. Brummer, · 2009
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Denoising of dynamic contrast-enhanced MR images using dynamic nonlocal means,
Y. Gal, A. J. H. Mehnert, A. P. Bradley, K. McMahon, D. Kennedy, S. Crozier, · 2010
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Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion,
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, · 2010
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Super-resolution mri using microscopic spatial modulation of magnetization.,
S. Ropele, F. Ebner, F. Fazekas, G. Reishofer, · 2010
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Medical image analysis with artificial neural networks.,
J. Jiang, P. Trundle, J. Ren, · 2010
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A survey of medical image registration on graphics hardware.,
O. Fluck, C. Vetter, W. Wein, A. Kamen, B. Preim, R. Westermann, · 2011
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Deformable medical image registration: setting the state of the art with discrete methods.,
B. Glocker, A. Sotiras, N. Komodakis, N. Paragios, · 2011
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A review of atlas-based segmentation for magnetic resonance brain images,
M. Cabezas, A. Oliver, X. Lladó, J. Freixenet, M. B. Cuadra, · 2011
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CBMIR: content-based image retrieval algorithm for medical image databases,
A. H. Pilevar, · 2011
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ImageNet classification with deep convolutional neural networks,
A. Krizhevsky, I. Sutskever, G. E. Hinton, · 2012
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A survey of GPU-based medical image computing techniques.,
L. Shi, W. Liu, H. Zhang, Y. Xie, D. Wang, · 2012
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A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory,
L. Hood, M. Flores, · 2012
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M. Lin, Q. Chen, S. Yan, · 2013
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Toward in vivo histology: a comparison of quantitative susceptibility mapping (QSM) with magnitude-, phase-, and R2*-imaging at ultra-high magnetic field strength,
A. Deistung, A. Schäfer, F. Schweser, U. Biedermann, R. Turner, J. R. Reichenbach, · 2013
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Magnetic resonance fingerprinting,
D. Ma, V. Gulani, N. Seiberlich, K. Liu, J. L. Sunshine, J. L. Duerk, M. A. Griswold, · 2013
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Classic models for dynamic contrast-enhanced mri.,
S. P. Sourbron, D. L. Buckley, · 2013
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Prospective motion correction in brain imaging: a review,
J. Maclaren, M. Herbst, O. Speck, M. Zaitsev, · 2013
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Medical image processing on the gpu - past, present and future.,
A. Eklund, P. Dufort, D. Forsberg, S. M. LaConte, · 2013
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Deformable medical image registration: a survey.,
A. Sotiras, C. Davatzikos, N. Paragios, · 2013
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Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging,
D. García-Lorenzo, S. Francis, S. Narayanan, D. L. Arnold, D. L. Collins, · 2013
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Content-based medical image retrieval: a survey of applications to multidimensional and multimodality dat.,
A. Kumar, J. Kim, W. Cai, M. Fulham, D. Feng, · 2013
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Striving for simplicity: The all convolutional net,
J. T. Springenberg, A. Dosovitskiy, T. Brox, M. Riedmiller, · 2014
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Very deep convolutional networks for large-scale image recognition,
K. Simonyan, A. Zisserman, · 2014
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Generative Adversarial Nets,
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, · 2014
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MR vascular fingerprinting: A new approach to compute cerebral blood volume, mean vessel radius, and oxygenation maps in the human brain,
T. Christen, N. A. Pannetier, W. W. Ni, D. Qiu, M. E. Moseley, N. Schuff, G. Zaharchuk, · 2014
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Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers,
G. Salimi-Khorshidi, G. Douaud, C. F. Beckmann, M. F. Glasser, L. Griffanti, S. M. Smith, · 2014
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Direct parametric reconstruction from undersampled (k,t)-space data in dynamic contrast enhanced MRI,
N. Dikaios, S. Arridge, V. Hamy, S. Punwani, D. Atkinson, · 2014
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In silico modeling of magnetic resonance flow imaging in complex vascular networks,
K. Jurczuk, M. Kretowski, P.-A. Eliat, H. Saint-Jalmes, J. Bezy-Wendling, · 2014
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Medical image registration: a review.,
F. P. M. Oliveira, J. M. R. S. Tavares, · 2014
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Artificial neural networks applied to taxi destination prediction,
A. De Brébisson, É. Simon, A. Auvolat, P. Vincent, Y. Bengio, · 2015
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Deep learning,
Y. LeCun, Y. Bengio, G. Hinton, · 2015
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M. A. Nielsen, Neural networks and deep learning, Determination Press, 2015
2015
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Fast and accurate deep network learning by exponential linear units (elus),
D.-A. Clevert, T. Unterthiner, S. Hochreiter, · 2015
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Magnetic resonance fingerprinting - a promising new approach to obtain standardized imaging biomarkers from mri.,
E. S. of Radiology (ESR), · 2015
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Denoising of 3D magnetic resonance images by using higher-order singular value decomposition.,
X. Zhang, Z. Xu, N. Jia, W. Yang, Q. Feng, W. Chen, Y. Feng, · 2015
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Medical image segmentation on GPUs–a comprehensive review.,
E. Smistad, T. L. Falch, M. Bozorgi, A. C. Elster, F. Lindseth, · 2015
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Content-based image retrieval for brain mri: an image-searching engine and population-based analysis to utilize past clinical data for future diagnosis,
A. V. Faria, K. Oishi, S. Yoshida, A. Hillis, M. I. Miller, S. Mori, · 2015
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A visual analytics approach using the exploration of multidimensional feature spaces for content-based medical image retrieval.,
A. Kumar, F. Nette, K. Klein, M. Fulham, J. Kim, · 2015
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Toward content based image retrieval with deep convolutional neural networks,
J. E. S. Sklan, A. J. Plassard, D. Fabbri, B. A. Landman, · 2015
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U-net: Convolutional networks for biomedical image segmentation,
O. Ronneberger, P.Fischer, T. Brox, · 2015
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Understanding neural networks through deep visualization,
J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, H. Lipson, · 2015
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A. Kendall, V. Badrinarayanan, R. Cipolla, · 2015
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WaveNet: A generative model for raw audio,
A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, K. Kavukcuoglu, · 2016
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Entity embeddings of categorical variables,
C. Guo, F. Berkhahn, · 2016
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Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning.,
H.-C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, R. M. Summers, · 2016
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Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,
V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, et al., · 2016
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2016
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Toward an Integration of Deep Learning and Neuroscience,
A. H. Marblestone, G. Wayne, K. P. Kording, · 2016
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Y. Gal, Uncertainty in deep learning, Ph.D. thesis, University of Cambridge, 2016
2016
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V-net: Fully convolutional neural networks for volumetric medical image segmentation,
F. Milletari, N. Navab, S.-A. Ahmadi, · 2016
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J. Redmon, Darknet: Open source neural networks in C,
2016
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Vision 20/20: Magnetic resonance imaging-guided attenuation correction in pet/mri: Challenges, solutions, and opportunities.,
A. Mehranian, H. Arabi, H. Zaidi, · 2016
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Learning clinically useful information from images: Past, present and future.,
D. Rueckert, B. Glocker, B. Kainz, · 2016
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Deep ADMM-Net for compressive sensing MRI,
Y. Yang, J. Sun, H. Li, Z. Xu, · 2016
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Accelerating magnetic resonance imaging via deep learning,
S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, D. Liang, · 2016
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Mr vascular fingerprinting in stroke and brain tumors models.,
B. Lemasson, N. Pannetier, N. Coquery, L. S. B. Boisserand, N. Collomb, N. Schuff, M. Moseley, G. Zaharchuk, E. L. Barbier, T. Christen, · 2016
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A mathematical motivation for complex-valued convolutional networks,
M. Tygert, J. Bruna, S. Chintala, Y. LeCun, S. Piantino, A. Szlam, · 2016
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q-space deep learning: Twelve-fold shorter and model-free diffusion MRI scans,
V. Golkov, A. Dosovitskiy, J. I. Sperl, M. I. Menzel, M. Czisch, P. Samann, T. Brox, D. Cremers, · 2016
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Image super-resolution: The techniques, applications, and future,
L. Yue, H. Shen, J. Li, Q. Yuan, H. Zhang, L. Zhang, · 2016
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Generative adversarial text to image synthesis,
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, H. Lee, · 2016
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A survey of medical image registration - under review.,
M. A. Viergever, J. B. A. Maintz, S. Klein, K. Murphy, M. Staring, J. P. W. Pluim, · 2016
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Scalable high-performance image registration framework by unsupervised deep feature representations learning.,
G. Wu, M. Kim, Q. Wang, B. C. Munsell, D. Shen, · 2016
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Fast and robust segmentation of the striatum using deep convolutional neural networks,
H. Choi, K. H. Jin, · 2016
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Deformable mr prostate segmentation via deep feature learning and sparse patch matching.,
Y. Guo, Y. Gao, D. Shen, · 2016
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Deep MRI brain extraction: A 3D convolutional neural network for skull stripping,
J. Kleesiek, G. Urban, A. Hubert, D. Schwarz, K. Maier-Hein, M. Bendszus, A. Biller, · 2016
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Endowing a content-based medical image retrieval system with perceptual similarity using ensemble strategy,
M. V. N. Bedo, D. Pereira Dos Santos, M. Ponciano-Silva, P. M. de Azevedo-Marques, A. P. d. L. Ferreira de Carvalho, C. Traina, · 2016
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Natural language processing in radiology: A systematic review,
E. Pons, L. M. M. Braun, M. G. M. Hunink, J. A. Kors, · 2016
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Understanding deep learning requires rethinking generalization,
C. Zhang, S. Bengio, M. Hardt, B. Recht, O. Vinyals, · 2016
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Semi-supervised knowledge transfer for deep learning from private training data,
N. Papernot, M. Abadi, U. Erlingsson, I. Goodfellow, K. Talwar, · 2016
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Can we open the black box of AI?,
D. Castelvecchi, · 2016
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Denoising gravitational waves using deep learning with recurrent denoising autoencoders,
H. Shen, D. George, E. Huerta, Z. Zhao, · 2017
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Theory-guided data science: A new paradigm for scientific discovery from data,
A. Karpatne, G. Atluri, J. H. Faghmous, M. Steinbach, A. Banerjee, A. Ganguly, S. Shekhar, N. Samatova, V. Kumar, · 2017
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Deep learning for health informatics.,
D. Ravi, C. Wong, F. Deligianni, M. Berthelot, J. Andreu-Perez, B. Lo, G.-Z. Yang, · 2017
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Identifying distinct subgroups of icu patients: A machine learning approach.,
K. C. Vranas, J. K. Jopling, T. E. Sweeney, M. C. Ramsey, A. S. Milstein, C. G. Slatore, G. J. Escobar, V. X. Liu, · 2017
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Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis,
B. Shickel, P. J. Tighe, A. Bihorac, P. Rashidi, · 2017
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Mastering the game of Go without human knowledge,
D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, et al., · 2017
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Dermatologist-level classification of skin cancer with deep neural networks,
A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, S. Thrun, · 2017
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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. W. M. van der Laak, B. van Ginneken, C. I. Sánchez, · 2017
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Neuroscience-Inspired Artificial Intelligence,
D. Hassabis, D. Kumaran, C. Summerfield, M. Botvinick, · 2017
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Neural network with unbounded activation functions is universal approximator,
S. Sonoda, N. Murata, · 2017
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Aggregated residual transformations for deep neural networks,
S. Xie, R. Girshick, P. Dollár, Z. Tu, K. He, · 2017
Cited alongside, same era.
Squeeze-and-excitation networks,
J. Hu, L. Shen, G. Sun, · 2017
Cited alongside, same era.
Learning transferable architectures for scalable image recognition,
B. Zoph, V. Vasudevan, J. Shlens, Q. V. Le, · 2017
Cited alongside, same era.
Neural optimizer search with reinforcement learning,
I. Bello, B. Zoph, V. Vasudevan, Q. V. Le, · 2017
Cited alongside, same era.
A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme.,
J. Lao, Y. Chen, Z.-C. Li, Q. Li, J. Zhang, J. Liu, G. Zhai, · 2017
Cited alongside, same era.
Opportunities and obstacles for deep learning in biology and medicine,
T. Ching, D. S. Himmelstein, B. K. Beaulieu-Jones, A. A. Kalinin, B. T. Do, G. P. Way, E. Ferrero, P.-M. Agapow, M. Zietz, M. M. Hoffman, W. Xie, G. L. Rosen, B. J. Lengerich, J. Israeli, J. Lanchantin, S. Woloszynek, A. E. Carpenter, A. Shrikumar, J. Xu, E. M. Cofer, C. A. Lavender, S. C. Turaga, A. M. Alexandari, Z. Lu, D. J. Harris, D. DeCaprio, Y. Qi, A. Kundaje, Y. Peng, L. K. Wiley, M. H. S. Segler, S. M. Boca, S. J. Swamidass, A. Huang, A. Gitter, C. S. Greene, · 2018
Closest in time.
Deep learning in radiology: an overview of the concepts and a survey of the state of the art,
M. A. Mazurowski, M. Buda, A. Saha, M. R. Bashir, · 2018
Closest in time.
Deep learning in radiology.,
M. P. McBee, O. A. Awan, A. T. Colucci, C. W. Ghobadi, N. Kadom, A. P. Kansagra, S. Tridandapani, W. F. Auffermann, · 2018
Closest in time.
Demystification of AI-driven medical image interpretation: past, present and future.,
P. Savadjiev, J. Chong, A. Dohan, M. Vakalopoulou, C. Reinhold, N. Paragios, B. Gallix, · 2018
Closest in time.
Artificial intelligence and machine learning in radiology: Opportunities, challenges, pitfalls, and criteria for success.,
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Precision radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework.,
L. Oakden-Rayner, G. Carneiro, T. Bessen, J. C. Nascimento, A. P. Bradley, L. J. Palmer, · 2017
Cited alongside, same era.
Quicksilver: Fast predictive image registration - a deep learning approach.,
X. Yang, R. Kwitt, M. Styner, M. Niethammer, · 2017
Cited alongside, same era.
Deep learning in medical imaging: General overview.,
J.-G. Lee, S. Jun, Y.-W. Cho, H. Lee, G. B. Kim, J. B. Seo, N. Kim, · 2017
Cited alongside, same era.
Deep learning: A primer for radiologists,
G. Chartrand, P. M. Cheng, E. Vorontsov, M. Drozdzal, S. Turcotte, C. J. Pal, S. Kadoury, A. Tang, · 2017
Cited alongside, same era.
Machine learning for medical imaging,
B. J. Erickson, P. Korfiatis, Z. Akkus, T. L. Kline, · 2017
Cited alongside, same era.
Toolkits and libraries for deep learning,
B. J. Erickson, P. Korfiatis, Z. Akkus, T. Kline, K. Philbrick, · 2017
Cited alongside, same era.
Deep learning for brain MRI segmentation: State of the art and future directions.,
Z. Akkus, A. Galimzianova, A. Hoogi, D. L. Rubin, B. J. Erickson, · 2017
Cited alongside, same era.
J. H. Thrall, X. Li, Q. Li, C. Cruz, S. Do, K. Dreyer, J. Brink, · 2018
Closest in time.
Convolutional neural networks: an overview and application in radiology.,
R. Yamashita, M. Nishio, R. K. G. Do, K. Togashi, · 2018
Closest in time.
Deep learning with convolutional neural network in radiology.,
K. Yasaka, H. Akai, A. Kunimatsu, S. Kiryu, O. Abe, · 2018
Closest in time.
Machine learning in medical imaging,
M. L. Giger, · 2018
Closest in time.
Deep learning in neuroradiology.,
G. Zaharchuk, E. Gong, M. Wintermark, D. Rubin, C. P. Langlotz, · 2018
Closest in time.
Deep learning guided stroke management: a review of clinical applications,
R. Feng, M. Badgeley, J. Mocco, E. K. Oermann, · 2018
Closest in time.
Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks,
J. R. Burt, N. Torosdagli, N. Khosravan, H. RaviPrakash, A. Mortazi, F. Tissavirasingham, S. Hussein, U. Bagci, · 2018
Closest in time.
A deep look into the future of quantitative imaging in oncology: A statement of working principles and proposal for change.,
O. Morin, M. Vallières, A. Jochems, H. C. Woodruff, G. Valdes, S. E. Braunstein, J. E. Wildberger, J. E. Villanueva-Meyer, V. Kearney, S. S. Yom, T. D. Solberg, P. Lambin, · 2018
Closest in time.
Data analysis strategies in medical imaging.,
C. Parmar, J. D. Barry, A. Hosny, J. Quackenbush, H. J. W. L. Aerts, · 2018
Closest in time.
Machine learning for medical ultrasound: status, methods, and future opportunities,
L. J. Brattain, B. A. Telfer, M. Dhyani, J. R. Grajo, A. E. Samir, · 2018
Closest in time.
Machine learning in ultrasound computer-aided diagnostic systems: A survey,
Q. Huang, F. Zhang, X. Li, · 2018
Closest in time.
Deep learning and its applications in biomedicine,
C. Cao, F. Liu, H. Tan, D. Song, W. Shu, W. Li, Y. Zhou, X. Bo, Z. Xie, · 2018
Closest in time.
Hello world deep learning in medical imaging.,
P. Lakhani, D. L. Gray, C. R. Pett, P. Nagy, G. Shih, · 2018
Closest in time.
Convolutional recurrent neural networks for dynamic mr image reconstruction.,
C. Qin, J. V. Hajnal, D. Rueckert, J. Schlemper, J. Caballero, A. N. Price, · 2018
Closest in time.
A deep cascade of convolutional neural networks for dynamic MR image reconstruction.,
J. Schlemper, J. Caballero, J. V. Hajnal, A. N. Price, D. Rueckert, · 2018
Closest in time.
Variable-density single-shot fast Spin-Echo MRI with deep learning reconstruction by using variational networks,
F. Chen, V. Taviani, I. Malkiel, J. Y. Cheng, J. I. Tamir, J. Shaikh, S. T. Chang, C. J. Hardy, J. M. Pauly, S. S. Vasanawala, · 2018
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Assessment of the generalization of learned image reconstruction and the potential for transfer learning,
F. Knoll, K. Hammernik, E. Kobler, T. Pock, M. P. Recht, D. K. Sodickson, · 2018
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Deep generative adversarial neural networks for compressive sensing (GANCS) MRI.,
M. Mardani, E. Gong, J. Y. Cheng, S. S. Vasanawala, G. Zaharchuk, L. Xing, J. M. Pauly, · 2018
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Image reconstruction by domain-transform manifold learning.,
B. Zhu, J. Z. Liu, S. F. Cauley, B. R. Rosen, M. S. Rosen, · 2018
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KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images,
T. Eo, Y. Jun, T. Kim, J. Jang, H.-J. Lee, D. Hwang, · 2018
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Deep learning with domain adaptation for accelerated projection-reconstruction MR,
Y. Han, J. Yoo, H. H. Kim, H. J. Shin, K. Sung, J. C. Ye, · 2018
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Super-resolution reconstruction of mr image with a novel residual learning network algorithm.,
J. Shi, Q. Liu, C. Wang, Q. Zhang, S. Ying, H. Xu, · 2018
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DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction.,
G. Yang, S. Yu, H. Dong, G. Slabaugh, P. L. Dragotti, X. Ye, F. Liu, S. Arridge, J. Keegan, Y. Guo, D. Firmin, J. Keegan, G. Slabaugh, S. Arridge, X. Ye, Y. Guo, S. Yu, F. Liu, D. Firmin, P. L. Dragotti, G. Yang, H. Dong, · 2018
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Quantitative susceptibility mapping using deep neural network: QSMnet.,
J. Yoon, E. Gong, I. Chatnuntawech, B. Bilgic, J. Lee, W. Jung, J. Ko, H. Jung, K. Setsompop, G. Zaharchuk, E. Y. Kim, J. Pauly, J. Lee, · 2018
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DeepQSM-Using Deep Learning to Solve the Dipole Inversion for MRI Susceptibility Mapping,
K. G. B. Rasmussen, M. J. Kristensen, R. G. Blendal, L. R. Ostergaard, M. Plocharski, K. O’Brien, C. Langkammer, A. Janke, M. Barth, S. Bollmann, · 2018
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Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guarantees,
V. Shah, C. Hegde, · 2018
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Using deep neural networks for inverse problems in imaging: beyond analytical methods,
A. Lucas, M. Iliadis, R. Molina, A. K. Katsaggelos, · 2018
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MoDL: Model Based Deep Learning Architecture for Inverse Problems,
H. K. Aggarwal, M. P. Mani, M. Jacob, · 2018
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NETT: Solving Inverse Problems with Deep Neural Networks,
H. Li, J. Schwab, S. Antholzer, M. Haltmeier, · 2018
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Fast 3D magnetic resonance fingerprinting for a whole-brain coverage,
D. Ma, Y. Jiang, Y. Chen, D. McGivney, B. Mehta, V. Gulani, M. Griswold, · 2018
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Time efficient whole-brain coverage with MR fingerprinting using slice-interleaved echo-planar-imaging,
B. Rieger, M. Akçakaya, J. C. Pariente, S. Llufriu, E. Martinez-Heras, S. Weingärtner, L. R. Schad, · 2018
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Estimation of perfusion properties with mr fingerprinting arterial spin labeling.,
K. L. Wright, Y. Jiang, D. Ma, D. C. Noll, M. A. Griswold, V. Gulani, L. Hernandez-Garcia, · 2018
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MR fingerprinting deep reconstruction network (DRONE),
O. Cohen, B. Zhu, M. S. Rosen, · 2018
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Learning implicit brain MRI manifolds with deep learning,
C. Bermudez, A. J. Plassard, T. L. Davis, A. T. Newton, S. M. Resnick, B. A. Landman, · 2018
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A convolutional neural network to filter artifacts in spectroscopic MRI,
S. S. Gurbani, E. Schreibmann, A. A. Maudsley, J. S. Cordova, B. J. Soher, H. Poptani, G. Verma, P. B. Barker, H. Shim, L. A. D. Cooper, · 2018
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Deep learning approaches for detection and removal of ghosting artifacts in MR spectroscopy,
S. P. Kyathanahally, A. Döring, R. Kreis, · 2018
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Automated reference-free detection of motion artifacts in magnetic resonance images,
T. Küstner, A. Liebgott, L. Mauch, P. Martirosian, F. Bamberg, K. Nikolaou, B. Yang, F. Schick, S. Gatidis, · 2018
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Simultaneous single- and multi-contrast super-resolution for brain MRI images based on a convolutional neural network.,
K. Zeng, H. Zheng, C. Cai, Y. Yang, K. Zhang, Z. Chen, · 2018
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Fusing multi-scale information in convolution network for mr image super-resolution reconstruction.,
C. Liu, X. Wu, X. Yu, Y. Tang, J. Zhang, J. Zhou, · 2018
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Super-resolution musculoskeletal MRI using deep learning,
A. S. Chaudhari, Z. Fang, F. Kogan, J. Wood, K. J. Stevens, E. K. Gibbons, J. H. Lee, G. E. Gold, B. A. Hargreaves, · 2018
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Quantitative magnetic resonance imaging phantoms: A review and the need for a system phantom,
K. E. Keenan, M. Ainslie, A. J. Barker, M. A. Boss, K. M. Cecil, C. Charles, T. L. Chenevert, L. Clarke, J. L. Evelhoch, P. Finn, D. Gembris, J. L. Gunter, D. L. G. Hill, C. R. Jack, E. F. Jackson, G. Liu, S. E. Russek, S. D. Sharma, M. Steckner, K. F. Stupic, J. D. Trzasko, C. Yuan, J. Zheng, · 2018
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A framework for the generation of realistic synthetic cardiac ultrasound and magnetic resonance imaging sequences from the same virtual patients.,
Y. Zhou, S. Giffard-Roisin, M. De Craene, S. Camarasu-Pop, J. D’Hooge, M. Alessandrini, D. Friboulet, M. Sermesant, O. Bernard, · 2018
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Model-based generation of large databases of cardiac images: Synthesis of pathological cine MR sequences from real healthy cases,
N. Duchateau, M. Sermesant, H. Delingette, N. Ayache, · 2018
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Generative adversarial networks: An overview,
A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, A. A. Bharath, · 2018
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An introduction to image synthesis with generative adversarial nets,
H. Huang, P. S. Yu, C. Wang, · 2018
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Text to image synthesis using generative adversarial networks,
C. Bodnar, · 2018
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Learning data augmentation for brain tumor segmentation with coarse-to-fine generative adversarial networks,
T. C. W. Mok, A. C. S. Chung, · 2018
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Synthesis of patient-specific transmission image for PET attenuation correction for PET/MR imaging of the brain using a convolutional neural network,
K. D. Spuhler, J. Gardus, Y. Gao, C. DeLorenzo, R. Parsey, C. Huang, · 2018
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Dixon-VIBE deep learning (DIVIDE) pseudo-CT synthesis for pelvis PET/MR attenuation correction,
A. Torrado-Carvajal, J. Vera-Olmos, D. Izquierdo-Garcia, O. A. Catalano, M. A. Morales, J. Margolin, A. Soricelli, M. Salvatore, N. Malpica, C. Catana, · 2018
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Synthetic data augmentation using GAN for improved liver lesion classification,
M. Frid-Adar, E. Klang, M. Amitai, J. Goldberger, H. Greenspan, · 2018
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Real-time deep pose estimation with geodesic loss for image-to-template rigid registration.,
S. S. M. Salehi, S. Khan, D. Erdogmus, A. Gholipour, · 2018
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3D/2D model-to-image registration by imitation learning for cardiac procedures.,
D. Toth, S. Miao, T. Kurzendorfer, C. A. Rinaldi, R. Liao, T. Mansi, K. Rhode, P. Mountney, · 2018
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Multi-modality cascaded convolutional neural networks for alzheimer’s disease diagnosis.,
M. Liu, D. Cheng, K. Wang, Y. Wang, A. D. N. Initiative, · 2018
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An unsupervised learning model for deformable medical image registration,
G. Balakrishnan, A. Zhao, M. R. Sabuncu, J. Guttag, A. V. Dalca, · 2018
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A deep learning framework for unsupervised affine and deformable image registration,
B. D. de Vos, F. F. Berendsen, M. A. Viergever, H. Sokooti, M. Staring, I. Isgum, · 2018
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Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: A systematic review,
H. R. Torres, S. Queiros, P. Morais, B. Oliveira, J. C. Fonseca, J. L. Vilaça, · 2018
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Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review,
J. Bernal, K. Kushibar, D. S. Asfaw, S. Valverde, A. Oliver, R. Martí, X. Lladó, · 2018
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Blood vessel segmentation algorithms - review of methods, datasets and evaluation metrics,
S. Moccia, E. De Momi, S. El Hadji, L. S. Mattos, · 2018
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A review on automatic fetal and neonatal brain MRI segmentation.,
A. Makropoulos, S. J. Counsell, D. Rueckert, · 2018
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3D multi-scale FCN with random modality voxel dropout learning for intervertebral disc localization and segmentation from multi-modality MR images,
X. Li, Q. Dou, H. Chen, C.-W. Fu, X. Qi, D. L. Belavý, G. Armbrecht, D. Felsenberg, G. Zheng, P.-A. Heng, · 2018
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A novel transfer learning approach to enhance deep neural network classification of brain functional connectomes.,
H. Li, N. A. Parikh, L. He, · 2018
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Multi-site diagnostic classification of schizophrenia using discriminant deep learning with functional connectivity MRI.,
L.-L. Zeng, H. Wang, P. Hu, B. Yang, W. Pu, H. Shen, X. Chen, Z. Liu, H. Yin, Q. Tan, K. Wang, D. Hu, · 2018
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TractSeg - fast and accurate white matter tract segmentation,
J. Wasserthal, P. Neher, K. H. Maier-Hein, · 2018
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Landmark-based deep multi-instance learning for brain disease diagnosis.,
M. Liu, J. Zhang, E. Adeli, D. Shen, · 2018
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Brain mri analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks,
J. Islam, Y. Zhang, · 2018
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Multimodal and multiscale deep neural networks for the early diagnosis of alzheimer’s disease using structural MR and FDG-PET images,
D. Lu, K. Popuri, G. W. Ding, R. Balachandar, Beg, M. Faisal, · 2018
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Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in mri.,
P. Moeskops, J. de Bresser, H. J. Kuijf, A. M. Mendrik, G. J. Biessels, J. P. W. Pluim, I. Išgum, · 2018
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Using deep learning algorithms to automatically identify the brain mri contrast: Implications for managing large databases.,
R. Pizarro, H.-E. Assemlal, D. De Nigris, C. Elliott, S. Antel, D. Arnold, A. Shmuel, · 2018
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Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI,
K. R. Laukamp, F. Thiele, G. Shakirin, D. Zopfs, A. Faymonville, M. Timmer, D. Maintz, M. Perkuhn, J. Borggrefe, · 2018
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Clinical evaluation of a multiparametric deep learning model for glioblastoma segmentation using heterogeneous magnetic resonance imaging data from clinical routine.,
M. Perkuhn, P. Stavrinou, F. Thiele, G. Shakirin, M. Mohan, D. Garmpis, C. Kabbasch, J. Borggrefe, · 2018
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Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing.,
E. A. AlBadawy, A. Saha, M. A. Mazurowski, · 2018
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Automatic semantic segmentation of brain gliomas from mri images using a deep cascaded neural network.,
S. Cui, L. Mao, J. Jiang, C. Liu, S. Xiong, · 2018
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Adaptahead optimization algorithm for learning deep cnn applied to mri segmentation.,
F. Hoseini, A. Shahbahrami, P. Bayat, · 2018
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Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls.,
Y. Yoo, L. Y. W. Tang, T. Brosch, D. K. B. Li, S. Kolind, I. Vavasour, A. Rauscher, A. L. MacKay, A. Traboulsee, R. C. Tam, · 2018
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Fully convolutional neural networks improve abdominal organ segmentation.,
M. F. Bobo, S. Bao, Y. Huo, Y. Yao, J. Virostko, A. J. Plassard, I. Lyu, A. Assad, R. G. Abramson, M. A. Hilmes, B. A. Landman, · 2018
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Computer-aided diagnostic system for early detection of acute renal transplant rejection using diffusion-weighted MRI,
M. Shehata, F. Khalifa, A. Soliman, M. Ghazal, F. Taher, M. Abou El-Ghar, A. Dwyer, G. Gimel’farb, R. Keynton, A. El-Baz, · 2018
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Computer-aided diagnosis of prostate cancer on magnetic resonance imaging using a convolutional neural network algorithm,
J. Ishioka, Y. Matsuoka, S. Uehara, Y. Yasuda, T. Kijima, S. Yoshida, M. Yokoyama, K. Saito, K. Kihara, N. Numao, T. Kimura, K. Kudo, I. Kumazawa, Y. Fujii, · 2018
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Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI,
Y. Song, Y.-D. Zhang, X. Yan, H. Liu, M. Zhou, B. Hu, G. Yang, · 2018
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J.-T. Lu, S. Pedemonte, B. Bizzo, S. Doyle, K. P. Andriole, M. H. Michalski, R. G. Gonzalez, S. R. Pomerantz, · 2018
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Automated pathogenesis-based diagnosis of lumbar neural foraminal stenosis via deep multiscale multitask learning.,
Z. Han, B. Wei, S. Leung, I. B. Nachum, D. Laidley, S. Li, · 2018
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Improving resolution of MR images with an adversarial network incorporating images with different contrast,
K. H. Kim, W.-J. Do, S.-H. Park, · 2018
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Overview on subjective similarity of images for content-based medical image retrieval,
C. Muramatsu, · 2018
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A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations,
A. B. Spanier, N. Caplan, J. Sosna, B. Acar, L. Joskowicz, · 2018
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Advanced deep-learning techniques for salient and category-specific object detection: A survey,
J. Han, D. Zhang, G. Cheng, N. Liu, D. Xu, · 2018
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Deepseek: Content based image search & retrieval,
T. Piplani, D. Bamman, · 2018
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Dynamic match kernel with deep convolutional features for image retrieval,
J. Yang, J. Liang, H. Shen, K. Wang, P. L. Rosin, M. Yang, · 2018
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Hybrid retrieval-generation reinforced agent for medical image report generation,
C. Y. Li, X. Liang, Z. Hu, E. P. Xing, · 2018
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Bimodal network architectures for automatic generation of image annotation from text,
M. Moradi, A. Madani, Y. Gur, Y. Guo, T. Syeda-Mahmood, · 2018
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Natural language-based machine learning models for the annotation of clinical radiology reports,
J. Zech, M. Pain, J. Titano, M. Badgeley, J. Schefflein, A. Su, A. Costa, J. Bederson, J. Lehar, E. K. Oermann, · 2018
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Automated radiology report summarization using an open-source natural language processing pipeline,
D. J. Goff, T. W. Loehfelm, · 2018
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NiftyNet: a deep-learning platform for medical imaging,
E. Gibson, W. Li, C. Sudre, L. Fidon, D. I. Shakir, G. Wang, Z. Eaton-Rosen, R. Gray, T. Doel, Y. Hu, T. Whyntie, P. Nachev, M. Modat, D. C. Barratt, S. Ourselin, M. J. Cardoso, T. Vercauteren, · 2018
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Deep learning: A critical appraisal,
G. Marcus, · 2018
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Troubling Trends in Machine Learning Scholarship (2018)
Z. C. Lipton, J. Steinhardt, · 2018
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Distributed learning of deep neural network over multiple agents,
O. Gupta, R. Raskar, · 2018
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Split learning for health: Distributed deep learning without sharing raw patient data,
P. Vepakomma, O. Gupta, T. Swedish, R. Raskar, · 2018
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Scalable Private Learning with PATE,
N. Papernot, S. Song, I. Mironov, A. Raghunathan, K. Talwar, Ú. Erlingsson, · 2018
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No Peek: A Survey of private distributed deep learning,
P. Vepakomma, T. Swedish, R. Raskar, O. Gupta, A. Dubey, · 2018
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Distributed deep learning networks among institutions for medical imaging,
K. Chang, N. Balachandar, C. Lam, D. Yi, J. Brown, A. Beers, B. Rosen, D. L. Rubin, J. Kalpathy-Cramer, · 2018
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Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study,
J. R. Zech, M. A. Badgeley, M. Liu, A. B. Costa, J. J. Titano, E. K. Oermann, · 2018
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A. Lundervold, K. Sprawka, A. Lundervold, Fast estimation of kidney volumes and time courses in DCE-MRI using convolutional neural networks, 2018
2018
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The building blocks of interpretability,
C. Olah, A. Satyanarayan, I. Johnson, S. Carter, L. Schubert, K. Ye, A. Mordvintsev, · 2018
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Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers,
F. M. Hohman, M. Kahng, R. Pienta, D. H. Chau, · 2018
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Show, attend and tell: Neural image caption generation with visual attention,
K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, Y. Bengio, · 2057
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Dropout Inference in Bayesian Neural Networks with Alpha-divergences,
Y. Li, Y. Gal, · 2061
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Face aging with conditional generative adversarial networks,
G. Antipov, M. Baccouche, J. Dugelay, · 2093
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