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Recent progress in Medical Artificial Intelligence (AI) has delivered systems that can reach clinical expert level performance.
“Med3D: Transfer Learning for 3D Medical Image Analysis”, 2019
Sihong Chen, Kai Ma and Yefeng Zheng · 1904
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“Med3D: Transfer Learning for 3D Medical Image Analysis”, 2019
Sihong Chen, Kai Ma and Yefeng Zheng · 1904
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“Med3D: Transfer Learning for 3D Medical Image Analysis”, 2019
Sihong Chen, Kai Ma and Yefeng Zheng · 1904
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“Classification and detection in mammograms with weak supervision via dual branch deep neural net”
Ran Bakalo, Rami Ben-Ari and Jacob Goldberger · 1909
Earlier work this paper cites.
“Classification and detection in mammograms with weak supervision via dual branch deep neural net”
Ran Bakalo, Rami Ben-Ari and Jacob Goldberger · 1909
Earlier work this paper cites.
“Classification and detection in mammograms with weak supervision via dual branch deep neural net”
Ran Bakalo, Rami Ben-Ari and Jacob Goldberger · 1909
Earlier work this paper cites.
“EfficientDet: Scalable and Efficient Object Detection”, 2020
Mingxing Tan, Ruoming Pang and Quoc. Le · 1911
Earlier work this paper cites.
“EfficientDet: Scalable and Efficient Object Detection”, 2020
Mingxing Tan, Ruoming Pang and Quoc. Le · 1911
Earlier work this paper cites.
“EfficientDet: Scalable and Efficient Object Detection”, 2020
Mingxing Tan, Ruoming Pang and Quoc. Le · 1911
Earlier work this paper cites.
“Self-organizing neural network that discovers surfaces in random-dot stereograms”
Suzanna Becker and Geoffrey Hinton · 1992
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“Self-organizing neural network that discovers surfaces in random-dot stereograms”
Suzanna Becker and Geoffrey Hinton · 1992
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“Self-organizing neural network that discovers surfaces in random-dot stereograms”
Suzanna Becker and Geoffrey Hinton · 1992
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“Screening for diabetic retinopathy: the wide-angle retinal camera”
Jacqueline Pugh, James Jacobson, WAJ Van, John Watters, Michael Tuley, David Lairson, Ronald Lorimor, Asha Kapadia and Ramon Velez · 1993
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“Screening for diabetic retinopathy: the wide-angle retinal camera”
Jacqueline Pugh, James Jacobson, WAJ Van, John Watters, Michael Tuley, David Lairson, Ronald Lorimor, Asha Kapadia and Ramon Velez · 1993
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“Screening for diabetic retinopathy: the wide-angle retinal camera”
Jacqueline Pugh, James Jacobson, WAJ Van, John Watters, Michael Tuley, David Lairson, Ronald Lorimor, Asha Kapadia and Ramon Velez · 1993
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“Improving generalization with active learning”
David Cohn, Les Atlas and Richard Ladner · 1994
Earlier work this paper cites.
“Improving generalization with active learning”
David Cohn, Les Atlas and Richard Ladner · 1994
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“Improving generalization with active learning”
David Cohn, Les Atlas and Richard Ladner · 1994
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“Generalization in reinforcement learning: Successful examples using sparse coarse coding”
Richard Sutton · 1996
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“Generalization in reinforcement learning: Successful examples using sparse coarse coding”
Richard Sutton · 1996
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“Generalization in reinforcement learning: Successful examples using sparse coarse coding”
Richard Sutton · 1996
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“Statistical Learning Theory”
Vladimir. Vapnik · 1998
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“Statistical Learning Theory”
Vladimir. Vapnik · 1998
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“Statistical Learning Theory”
Vladimir. Vapnik · 1998
Earlier work this paper cites.
“Statistical Learning Theory”
Vladimir. Vapnik · 1998
Earlier work this paper cites.
“Statistical Learning Theory”
Vladimir. Vapnik · 1998
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“Statistical Learning Theory”
Vladimir. Vapnik · 1998
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“Semi-supervised learning using gaussian fields and harmonic functions”
Xiaojin Zhu, Zoubin Ghahramani and John Lafferty · 2003
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“Semi-supervised learning using gaussian fields and harmonic functions”
Xiaojin Zhu, Zoubin Ghahramani and John Lafferty · 2003
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“Semi-supervised learning using gaussian fields and harmonic functions”
Xiaojin Zhu, Zoubin Ghahramani and John Lafferty · 2003
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“Detection of diabetic foveal edema: contact lens biomicroscopy compared with optical coherence tomography”
Justin Brown, Sharon Solomon, Susan Bressler, Andrew Schachat, Cathy DiBernardo and Neil Bressler · 2004
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“Detection of diabetic foveal edema: contact lens biomicroscopy compared with optical coherence tomography”
Justin Brown, Sharon Solomon, Susan Bressler, Andrew Schachat, Cathy DiBernardo and Neil Bressler · 2004
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“Detection of diabetic foveal edema: contact lens biomicroscopy compared with optical coherence tomography”
Justin Brown, Sharon Solomon, Susan Bressler, Andrew Schachat, Cathy DiBernardo and Neil Bressler · 2004
Earlier work this paper cites.
“Detection of diabetic foveal edema: contact lens biomicroscopy compared with optical coherence tomography”
Justin Brown, Sharon Solomon, Susan Bressler, Andrew Schachat, Cathy DiBernardo and Neil Bressler · 2004
Earlier work this paper cites.
“Detection of diabetic foveal edema: contact lens biomicroscopy compared with optical coherence tomography”
Justin Brown, Sharon Solomon, Susan Bressler, Andrew Schachat, Cathy DiBernardo and Neil Bressler · 2004
Earlier work this paper cites.
“Detection of diabetic foveal edema: contact lens biomicroscopy compared with optical coherence tomography”
Justin Brown, Sharon Solomon, Susan Bressler, Andrew Schachat, Cathy DiBernardo and Neil Bressler · 2004
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“One-shot learning of object categories”
Fei-Fei Li, Rob Fergus and Pietro Perona · 2006
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“Automated detection of clinically significant macular edema by grid scanning optical coherence tomography”
Srinivas Sadda, Ou Tan, Alexander Walsh, Joel Schuman, Rohit Varma and David Huang · 2006
Earlier work this paper cites.
“Automated detection of clinically significant macular edema by grid scanning optical coherence tomography”
Srinivas Sadda, Ou Tan, Alexander Walsh, Joel Schuman, Rohit Varma and David Huang · 2006
Earlier work this paper cites.
“One-shot learning of object categories”
Fei-Fei Li, Rob Fergus and Pietro Perona · 2006
Earlier work this paper cites.
“Automated detection of clinically significant macular edema by grid scanning optical coherence tomography”
Srinivas Sadda, Ou Tan, Alexander Walsh, Joel Schuman, Rohit Varma and David Huang · 2006
Earlier work this paper cites.
“Automated detection of clinically significant macular edema by grid scanning optical coherence tomography”
Srinivas Sadda, Ou Tan, Alexander Walsh, Joel Schuman, Rohit Varma and David Huang · 2006
Earlier work this paper cites.
“One-shot learning of object categories”
Fei-Fei Li, Rob Fergus and Pietro Perona · 2006
Earlier work this paper cites.
“Automated detection of clinically significant macular edema by grid scanning optical coherence tomography”
Srinivas Sadda, Ou Tan, Alexander Walsh, Joel Schuman, Rohit Varma and David Huang · 2006
Earlier work this paper cites.
“Automated detection of clinically significant macular edema by grid scanning optical coherence tomography”
Srinivas Sadda, Ou Tan, Alexander Walsh, Joel Schuman, Rohit Varma and David Huang · 2006
Earlier work this paper cites.
“Visualizing data using t-SNE.”
Laurens Van and Geoffrey Hinton · 2008
Earlier work this paper cites.
“Visualizing data using t-SNE.”
Laurens Van and Geoffrey Hinton · 2008
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“Visualizing data using t-SNE.”
Laurens Van and Geoffrey Hinton · 2008
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“Imagenet: A large-scale hierarchical image database”
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei · 2009
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“Imagenet: A large-scale hierarchical image database”
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei · 2009
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“Timed efficiency of interpretation of digital and film-screen screening mammograms”
Tamara Haygood, Jihong Wang, E Atkinson, Deanna Lane, Tanya Stephens, Parul Patel and Gary Whitman · 2009
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“Imagenet: A large-scale hierarchical image database”
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei · 2009
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“Imagenet: A large-scale hierarchical image database”
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei · 2009
Earlier work this paper cites.
“Timed efficiency of interpretation of digital and film-screen screening mammograms”
Tamara Haygood, Jihong Wang, E Atkinson, Deanna Lane, Tanya Stephens, Parul Patel and Gary Whitman · 2009
Earlier work this paper cites.
“Imagenet: A large-scale hierarchical image database”
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei · 2009
Earlier work this paper cites.
“Imagenet: A large-scale hierarchical image database”
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei · 2009
Earlier work this paper cites.
“Timed efficiency of interpretation of digital and film-screen screening mammograms”
Tamara Haygood, Jihong Wang, E Atkinson, Deanna Lane, Tanya Stephens, Parul Patel and Gary Whitman · 2009
Earlier work this paper cites.
“Representation learning: A review and new perspectives”
Yoshua Bengio, Aaron Courville and Pascal Vincent · 2013
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“Domain generalization via invariant feature representation”
Krikamol Muandet, David Balduzzi and Bernhard Schölkopf · 2013
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“ACR BI-RADS atlas: breast imaging reporting and data system; mammography, ultrasound, magnetic resonance imaging, follow-up and outcome monitoring, data dictionary”
American of Radiology and Carl D’Orsi · 2013
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“Representation learning: A review and new perspectives”
Yoshua Bengio, Aaron Courville and Pascal Vincent · 2013
Earlier work this paper cites.
“Domain generalization via invariant feature representation”
Krikamol Muandet, David Balduzzi and Bernhard Schölkopf · 2013
Earlier work this paper cites.
“ACR BI-RADS atlas: breast imaging reporting and data system; mammography, ultrasound, magnetic resonance imaging, follow-up and outcome monitoring, data dictionary”
American of Radiology and Carl D’Orsi · 2013
Earlier work this paper cites.
“Representation learning: A review and new perspectives”
Yoshua Bengio, Aaron Courville and Pascal Vincent · 2013
Earlier work this paper cites.
“Domain generalization via invariant feature representation”
Krikamol Muandet, David Balduzzi and Bernhard Schölkopf · 2013
Earlier work this paper cites.
“ACR BI-RADS atlas: breast imaging reporting and data system; mammography, ultrasound, magnetic resonance imaging, follow-up and outcome monitoring, data dictionary”
American of Radiology and Carl D’Orsi · 2013
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“Adam: A method for stochastic optimization”
Diederik Kingma and Jimmy Ba · 2014
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“Adam: A method for stochastic optimization”
Diederik Kingma and Jimmy Ba · 2014
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“Adam: A method for stochastic optimization”
Diederik Kingma and Jimmy Ba · 2014
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“Unsupervised visual representation learning by context prediction”
Carl Doersch, Abhinav Gupta and Alexei Efros · 2015
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“Deep learning”
Yann LeCun, Yoshua Bengio and Geoffrey Hinton · 2015
Earlier work this paper cites.
“Imagenet large scale visual recognition challenge”
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla and Michael Bernstein · 2015
Earlier work this paper cites.
“Optical coherence tomography (OCT) for detection of macular oedema in patients with diabetic retinopathy”
Gianni Virgili, Francesca Menchini, Giovanni Casazza, Ruth Hogg, Radha Das, Xue Wang and Manuele Michelessi · 2015
Earlier work this paper cites.
“Unsupervised visual representation learning by context prediction”
Carl Doersch, Abhinav Gupta and Alexei Efros · 2015
Earlier work this paper cites.
“Unsupervised domain adaptation by backpropagation”
Yaroslav Ganin and Victor Lempitsky · 2015
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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
Earlier work this paper cites.
“Imagenet large scale visual recognition challenge”
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla and Michael Bernstein · 2015
Earlier work this paper cites.
“Going deeper with convolutions”
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke and Andrew Rabinovich · 2015
Earlier work this paper cites.
URL: https://www.tensorflow.org/
“TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems”, 2015 · 2015
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“Optical coherence tomography (OCT) for detection of macular oedema in patients with diabetic retinopathy”
Gianni Virgili, Francesca Menchini, Giovanni Casazza, Ruth Hogg, Radha Das, Xue Wang and Manuele Michelessi · 2015
Earlier work this paper cites.
“Unsupervised visual representation learning by context prediction”
Carl Doersch, Abhinav Gupta and Alexei Efros · 2015
Earlier work this paper cites.
“Deep learning”
Yann LeCun, Yoshua Bengio and Geoffrey Hinton · 2015
Earlier work this paper cites.
“Imagenet large scale visual recognition challenge”
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla and Michael Bernstein · 2015
Earlier work this paper cites.
“Optical coherence tomography (OCT) for detection of macular oedema in patients with diabetic retinopathy”
Gianni Virgili, Francesca Menchini, Giovanni Casazza, Ruth Hogg, Radha Das, Xue Wang and Manuele Michelessi · 2015
Earlier work this paper cites.
“Unsupervised visual representation learning by context prediction”
Carl Doersch, Abhinav Gupta and Alexei Efros · 2015
Earlier work this paper cites.
“Unsupervised domain adaptation by backpropagation”
Yaroslav Ganin and Victor Lempitsky · 2015
Earlier work this paper cites.
“Batch normalization: Accelerating deep network training by reducing internal covariate shift”
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
“U-net: Convolutional networks for biomedical image segmentation”
Olaf Ronneberger, Philipp Fischer and Thomas Brox · 2015
Earlier work this paper cites.
“Imagenet large scale visual recognition challenge”
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla and Michael Bernstein · 2015
Earlier work this paper cites.
“Going deeper with convolutions”
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke and Andrew Rabinovich · 2015
Earlier work this paper cites.
URL: https://www.tensorflow.org/
“TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems”, 2015 · 2015
Earlier work this paper cites.
“Optical coherence tomography (OCT) for detection of macular oedema in patients with diabetic retinopathy”
Gianni Virgili, Francesca Menchini, Giovanni Casazza, Ruth Hogg, Radha Das, Xue Wang and Manuele Michelessi · 2015
Earlier work this paper cites.
“Unsupervised visual representation learning by context prediction”
Carl Doersch, Abhinav Gupta and Alexei Efros · 2015
Earlier work this paper cites.
“Deep learning”
Yann LeCun, Yoshua Bengio and Geoffrey Hinton · 2015
Earlier work this paper cites.
“Imagenet large scale visual recognition challenge”
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla and Michael Bernstein · 2015
Earlier work this paper cites.
“Optical coherence tomography (OCT) for detection of macular oedema in patients with diabetic retinopathy”
Gianni Virgili, Francesca Menchini, Giovanni Casazza, Ruth Hogg, Radha Das, Xue Wang and Manuele Michelessi · 2015
Earlier work this paper cites.
“Unsupervised visual representation learning by context prediction”
Carl Doersch, Abhinav Gupta and Alexei Efros · 2015
Earlier work this paper cites.
“Unsupervised domain adaptation by backpropagation”
Yaroslav Ganin and Victor Lempitsky · 2015
Earlier work this paper cites.
“Batch normalization: Accelerating deep network training by reducing internal covariate shift”
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
“U-net: Convolutional networks for biomedical image segmentation”
Olaf Ronneberger, Philipp Fischer and Thomas Brox · 2015
Earlier work this paper cites.
“Imagenet large scale visual recognition challenge”
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla and Michael Bernstein · 2015
Earlier work this paper cites.
“Going deeper with convolutions”
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke and Andrew Rabinovich · 2015
Earlier work this paper cites.
URL: https://www.tensorflow.org/
“TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems”, 2015 · 2015
Earlier work this paper cites.
“Optical coherence tomography (OCT) for detection of macular oedema in patients with diabetic retinopathy”
Gianni Virgili, Francesca Menchini, Giovanni Casazza, Ruth Hogg, Radha Das, Xue Wang and Manuele Michelessi · 2015
Earlier work this paper cites.
“Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs”
Varun Gulshan, Lily Peng, Marc Coram, Martin Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams and Jorge Cuadros · 2016
Earlier work this paper cites.
“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Earlier work this paper cites.
“What makes ImageNet good for transfer learning?”
Minyoung Huh, Pulkit Agrawal and Alexei Efros · 2016
Earlier work this paper cites.
“Context encoders: Feature learning by inpainting”
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell and Alexei Efros · 2016
Earlier work this paper cites.
“Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs”
Varun Gulshan, Lily Peng, Marc Coram, Martin Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams and Jorge Cuadros · 2016
Earlier work this paper cites.
“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Earlier work this paper cites.
“Identity mappings in deep residual networks”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Earlier work this paper cites.
“Sgdr: Stochastic gradient descent with warm restarts”
Ilya Loshchilov and Frank Hutter · 2016
Earlier work this paper cites.
“Unsupervised learning of visual representations by solving jigsaw puzzles”
Mehdi Noroozi and Paolo Favaro · 2016
Earlier work this paper cites.
“Colorful image colorization”
Richard Zhang, Phillip Isola and Alexei Efros · 2016
Earlier work this paper cites.
“Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs”
Varun Gulshan, Lily Peng, Marc Coram, Martin Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams and Jorge Cuadros · 2016
Earlier work this paper cites.
“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Earlier work this paper cites.
“What makes ImageNet good for transfer learning?”
Minyoung Huh, Pulkit Agrawal and Alexei Efros · 2016
Earlier work this paper cites.
“Context encoders: Feature learning by inpainting”
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell and Alexei Efros · 2016
Earlier work this paper cites.
“Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs”
Varun Gulshan, Lily Peng, Marc Coram, Martin Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams and Jorge Cuadros · 2016
Earlier work this paper cites.
“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Earlier work this paper cites.
“Identity mappings in deep residual networks”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Earlier work this paper cites.
“Sgdr: Stochastic gradient descent with warm restarts”
Ilya Loshchilov and Frank Hutter · 2016
Earlier work this paper cites.
“Unsupervised learning of visual representations by solving jigsaw puzzles”
Mehdi Noroozi and Paolo Favaro · 2016
Earlier work this paper cites.
“Colorful image colorization”
Richard Zhang, Phillip Isola and Alexei Efros · 2016
Earlier work this paper cites.
“Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs”
Varun Gulshan, Lily Peng, Marc Coram, Martin Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams and Jorge Cuadros · 2016
Earlier work this paper cites.
“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Earlier work this paper cites.
“What makes ImageNet good for transfer learning?”
Minyoung Huh, Pulkit Agrawal and Alexei Efros · 2016
Earlier work this paper cites.
“Context encoders: Feature learning by inpainting”
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell and Alexei Efros · 2016
Earlier work this paper cites.
“Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs”
Varun Gulshan, Lily Peng, Marc Coram, Martin Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams and Jorge Cuadros · 2016
Earlier work this paper cites.
“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Earlier work this paper cites.
“Identity mappings in deep residual networks”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Earlier work this paper cites.
“Sgdr: Stochastic gradient descent with warm restarts”
Ilya Loshchilov and Frank Hutter · 2016
Earlier work this paper cites.
“Unsupervised learning of visual representations by solving jigsaw puzzles”
Mehdi Noroozi and Paolo Favaro · 2016
Earlier work this paper cites.
“Colorful image colorization”
Richard Zhang, Phillip Isola and Alexei Efros · 2016
Earlier work this paper cites.
“Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer”
Babak Bejnordi, Mitko Veta, Paul Van, Bram Van, Nico Karssemeijer, Geert Litjens, Jeroen Van, Meyke Hermsen, Quirine Manson and Maschenka Balkenhol · 2017
Earlier work this paper cites.
“Dermatologist-level classification of skin cancer with deep neural networks”
Andre Esteva, Brett Kuprel, Roberto Novoa, Justin Ko, Susan Swetter, Helen Blau and Sebastian Thrun · 2017
Earlier work this paper cites.
“Colorization as a proxy task for visual understanding”
Gustav Larsson, Michael Maire and Gregory Shakhnarovich · 2017
Earlier work this paper cites.
“Revisiting unreasonable effectiveness of data in deep learning era”
Chen Sun, Abhinav Shrivastava, Saurabh Singh and Abhinav Gupta · 2017
Earlier work this paper cites.
“mixup: Beyond empirical risk minimization”
Hongyi Zhang, Moustapha Cisse, Yann Dauphin and David Lopez-Paz · 2017
Earlier work this paper cites.
“Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer”
Babak Bejnordi, Mitko Veta, Paul Van, Bram Van, Nico Karssemeijer, Geert Litjens, Jeroen Van, Meyke Hermsen, Quirine Manson and Maschenka Balkenhol · 2017
Earlier work this paper cites.
“Deep learning”
Yoshua Bengio, Ian Goodfellow and Aaron Courville · 2017
Earlier work this paper cites.
“Dermatologist-level classification of skin cancer with deep neural networks”
Andre Esteva, Brett Kuprel, Roberto Novoa, Justin Ko, Susan Swetter, Helen Blau and Sebastian Thrun · 2017
Earlier work this paper cites.
“Accurate, large minibatch sgd: Training imagenet in 1 hour”
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia and Kaiming He · 2017
Earlier work this paper cites.
“Knowledge transfer for melanoma screening with deep learning”
Afonso Menegola, Michel Fornaciali, Ramon Pires, Flávia Bittencourt, Sandra Avila and Eduardo Valle · 2017
Earlier work this paper cites.
“Unified deep supervised domain adaptation and generalization”
Saeid Motiian, Marco Piccirilli, Donald Adjeroh and Gianfranco Doretto · 2017
Earlier work this paper cites.
“Revisiting unreasonable effectiveness of data in deep learning era”
Chen Sun, Abhinav Shrivastava, Saurabh Singh and Abhinav Gupta · 2017
Earlier work this paper cites.
“Large batch training of convolutional networks”
Yang You, Igor Gitman and Boris Ginsburg · 2017
Earlier work this paper cites.
“mixup: Beyond empirical risk minimization”
Hongyi Zhang, Moustapha Cisse, Yann Dauphin and David Lopez-Paz · 2017
Earlier work this paper cites.
“Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer”
Babak Bejnordi, Mitko Veta, Paul Van, Bram Van, Nico Karssemeijer, Geert Litjens, Jeroen Van, Meyke Hermsen, Quirine Manson and Maschenka Balkenhol · 2017
Earlier work this paper cites.
“Dermatologist-level classification of skin cancer with deep neural networks”
Andre Esteva, Brett Kuprel, Roberto Novoa, Justin Ko, Susan Swetter, Helen Blau and Sebastian Thrun · 2017
Earlier work this paper cites.
“Colorization as a proxy task for visual understanding”
Gustav Larsson, Michael Maire and Gregory Shakhnarovich · 2017
Earlier work this paper cites.
“Revisiting unreasonable effectiveness of data in deep learning era”
Chen Sun, Abhinav Shrivastava, Saurabh Singh and Abhinav Gupta · 2017
Earlier work this paper cites.
“mixup: Beyond empirical risk minimization”
Hongyi Zhang, Moustapha Cisse, Yann Dauphin and David Lopez-Paz · 2017
Earlier work this paper cites.
“Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer”
Babak Bejnordi, Mitko Veta, Paul Van, Bram Van, Nico Karssemeijer, Geert Litjens, Jeroen Van, Meyke Hermsen, Quirine Manson and Maschenka Balkenhol · 2017
Earlier work this paper cites.
“Deep learning”
Yoshua Bengio, Ian Goodfellow and Aaron Courville · 2017
Earlier work this paper cites.
“Dermatologist-level classification of skin cancer with deep neural networks”
Andre Esteva, Brett Kuprel, Roberto Novoa, Justin Ko, Susan Swetter, Helen Blau and Sebastian Thrun · 2017
Earlier work this paper cites.
“Accurate, large minibatch sgd: Training imagenet in 1 hour”
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia and Kaiming He · 2017
Earlier work this paper cites.
“Knowledge transfer for melanoma screening with deep learning”
Afonso Menegola, Michel Fornaciali, Ramon Pires, Flávia Bittencourt, Sandra Avila and Eduardo Valle · 2017
Earlier work this paper cites.
“Unified deep supervised domain adaptation and generalization”
Saeid Motiian, Marco Piccirilli, Donald Adjeroh and Gianfranco Doretto · 2017
Earlier work this paper cites.
“Revisiting unreasonable effectiveness of data in deep learning era”
Chen Sun, Abhinav Shrivastava, Saurabh Singh and Abhinav Gupta · 2017
Earlier work this paper cites.
“Large batch training of convolutional networks”
Yang You, Igor Gitman and Boris Ginsburg · 2017
Earlier work this paper cites.
“mixup: Beyond empirical risk minimization”
Hongyi Zhang, Moustapha Cisse, Yann Dauphin and David Lopez-Paz · 2017
Earlier work this paper cites.
“Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer”
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Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova · 2018
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Spyros Gidaris, Praveer Singh and Nikos Komodakis · 2018
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Maximilian Ilse, Jakub Tomczak and Max Welling · 2018
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Haoran Zhang, Natalie Dullerud, Laleh Seyyed-Kalantari, Quaid Morris, Shalmali Joshi and Marzyeh Ghassemi · 2021
Later among the works it cites.
“A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises”
S Zhou, Hayit Greenspan, Christos Davatzikos, James Duncan, Bram Van, Anant Madabhushi, Jerry Prince, Daniel Rueckert and Ronald Summers · 2021
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“The evolution of out-of-distribution robustness throughout fine-tuning”
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Later among the works it cites.
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Shekoofeh Azizi, Basil Mustafa, Fiona Ryan, Zachary Beaver, Jan Freyberg, Jonathan Deaton, Aaron Loh, Alan Karthikesalingam, Simon Kornblith and Ting Chen · 2021
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Later among the works it cites.
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Later among the works it cites.
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Basil Mustafa, Aaron Loh, Jan Freyberg, Patricia MacWilliams, Megan Wilson, Scott McKinney, Marcin Sieniek, Jim Winkens, Yuan Liu and Peggy Bui · 2021
Later among the works it cites.
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Alec Radford, Jong Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin and Jack Clark · 2021
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Alexander Robey, George Pappas and Hamed Hassani · 2021
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“Toward causal representation learning”
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Ke, Nal Kalchbrenner, Anirudh Goyal and Yoshua Bengio · 2021
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Later among the works it cites.
“Contrastive learning of heart and lung sounds for label-efficient diagnosis”
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Later among the works it cites.
“MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models”
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Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf and Stefan Bauer · 2021
Later among the works it cites.
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Later among the works it cites.
Yen Vu, Richard Wang, Niranjan Balachandar, Can Liu, Andrew Ng and Pranav Rajpurkar · 2021
Later among the works it cites.
“Generalizing to Unseen Domains: A Survey on Domain Generalization”
Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Wenjun Zeng and Tao Qin · 2021
Later among the works it cites.
“Robust fine-tuning of zero-shot models”
Mitchell Wortsman, Gabriel Ilharco, Mike Li, Jong Kim, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong and Ludwig Schmidt · 2021
Later among the works it cites.
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Ellery Wulczyn, David Steiner, Melissa Moran, Markus Plass, Robert Reihs, Fraser Tan, Isabelle Flament-Auvigne, Trissia Brown, Peter Regitnig, Po-Hsuan Chen, Narayan Hegde, Apaar Sadhwani, Robert MacDonald, Benny Ayalew, Greg. Corrado, Lily. Peng, Daniel Tse, Heimo Müller, Zhaoyang Xu, Yun Liu, Martin. Stumpe, Kurt Zatloukal and Craig. Mermel · 2021
Later among the works it cites.
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Mengyue Yang, Furui Liu, Zhitang Chen, Xinwei Shen, Jianye Hao and Jun Wang · 2021
Later among the works it cites.
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Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang and Chen Loy · 2021
Later among the works it cites.
“PASS: An ImageNet replacement for self-supervised pretraining without humans”
Yuki. Asano, Christian Rupprecht, Andrew Zisserman and Andrea Vedaldi · 2021
Later among the works it cites.
“Big self-supervised models advance medical image classification”
Shekoofeh Azizi, Basil Mustafa, Fiona Ryan, Zachary Beaver, Jan Freyberg, Jonathan Deaton, Aaron Loh, Alan Karthikesalingam, Simon Kornblith and Ting Chen · 2021
Later among the works it cites.
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Basil Mustafa, Aaron Loh, Jan Freyberg, Patricia MacWilliams, Megan Wilson, Scott McKinney, Marcin Sieniek, Jim Winkens, Yuan Liu and Peggy Bui · 2021
Later among the works it cites.
“Big self-supervised models advance medical image classification”
Shekoofeh Azizi, Basil Mustafa, Fiona Ryan, Zachary Beaver, Jan Freyberg, Jonathan Deaton, Aaron Loh, Alan Karthikesalingam, Simon Kornblith and Ting Chen · 2021
Later among the works it cites.
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Later among the works it cites.
“Using AI to help find answers to common skin conditions”, 2021
Peggy Bui and Yuan Liu · 2021
Later among the works it cites.
“Exploring simple siamese representation learning”
Xinlei Chen and Kaiming He · 2021
Later among the works it cites.
“Replication of an open-access deep learning system for screening mammography: Reduced performance mitigated by retraining on local data”
James Condon, Luke Oakden-Rayner, Kelly Hall, Michelle Reintals, Andrew Holmes, Gustavo Carneiro and Lyle Palmer · 2021
Later among the works it cites.
“An image is worth 16x16 words: Transformers for image recognition at scale”
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold and Sylvain Gelly · 2021
Later among the works it cites.
“How Well Do Self-Supervised Models Transfer?”
Linus Ericsson, Henry Gouk and Timothy Hospedales · 2021
Later among the works it cites.
“Multi-task weak supervision enables anatomically-resolved abnormality detection in whole-body FDG-PET/CT”
Sabri Eyuboglu, Geoffrey Angus, Bhavik Patel, Anuj Pareek, Guido Davidzon, Jin Long, Jared Dunnmon and Matthew Lungren · 2021
Later among the works it cites.
“Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy”
Karoline Freeman, Julia Geppert, Chris Stinton, Daniel Todkill, Samantha Johnson, Aileen Clarke and Sian Taylor-Phillips · 2021
Later among the works it cites.
“Self-supervised pretraining of visual features in the wild”
Priya Goyal, Mathilde Caron, Benjamin Lefaudeux, Min Xu, Pengchao Wang, Vivek Pai, Mannat Singh, Vitaliy Liptchinsky, Ishan Misra and Armand Joulin · 2021
Later among the works it cites.
“Using artificial intelligence in breast cancer screening”, 2021
Sunny Jansen and Krish Eswaran · 2021
Later among the works it cites.
“Achieving fairness in medical devices”
Achuta Kadambi · 2021
Later among the works it cites.
“SSLP: Spatial Guided Self-supervised Learning on Pathological Images”
Jiajun Li, Tiancheng Lin and Yi Xu · 2021
Later among the works it cites.
“Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning”
Zheren Li, Zhiming Cui, Sheng Wang, Yuji Qi, Xi Ouyang, Qitian Chen, Yuezhi Yang, Zhong Xue, Dinggang Shen and Jie-Zhi Cheng · 2021
Later among the works it cites.
“Supervised transfer learning at scale for medical imaging”
Basil Mustafa, Aaron Loh, Jan Freyberg, Patricia MacWilliams, Megan Wilson, Scott McKinney, Marcin Sieniek, Jim Winkens, Yuan Liu and Peggy Bui · 2021
Later among the works it cites.
“An algorithmic approach to reducing unexplained pain disparities in underserved populations”
Emma Pierson, David Cutler, Jure Leskovec, Sendhil Mullainathan and Ziad Obermeyer · 2021
Later among the works it cites.
“Current and future applications of artificial intelligence in pathology: a clinical perspective”
Emad Rakha, Michael Toss, Sho Shiino, Paul Gamble, Ronnachai Jaroensri, Craig Mermel and Po-Hsuan Chen · 2021
Later among the works it cites.
“Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans”
Michael Roberts, Derek Driggs, Matthew Thorpe, Julian Gilbey, Michael Yeung, Stephan Ursprung, Angelica Aviles-Rivero, Christian Etmann, Cathal McCague and Lucian Beer · 2021
Later among the works it cites.
“MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models”
Hari Sowrirajan, Jingbo Yang, Andrew Ng and Pranav Rajpurkar · 2021
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