A citation-based method for searching scientific literature


List of co-cited articles
277 articles co-cited >1



Times Cited
  Times     Co-cited
Similarity


Deep learning.
Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Nature 2015
28

Deep learning in neural networks: an overview.
Jürgen Schmidhuber. Neural Netw 2015
21


Long short-term memory.
S Hochreiter, J Schmidhuber. Neural Comput 1997
18

A survey on deep learning in medical image analysis.
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A W M van der Laak, Bram van Ginneken, Clara I Sánchez. Med Image Anal 2017
12


Performance-optimized hierarchical models predict neural responses in higher visual cortex.
Daniel L K Yamins, Ha Hong, Charles F Cadieu, Ethan A Solomon, Darren Seibert, James J DiCarlo. Proc Natl Acad Sci U S A 2014
458
11


Mastering the game of Go with deep neural networks and tree search.
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot,[...]. Nature 2016
914
10

Dermatologist-level classification of skin cancer with deep neural networks.
Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, Sebastian Thrun. Nature 2017
10

Hierarchical models of object recognition in cortex.
M Riesenhuber, T Poggio. Nat Neurosci 1999
9

Reducing the dimensionality of data with neural networks.
G E Hinton, R R Salakhutdinov. Science 2006
9

A fast learning algorithm for deep belief nets.
Geoffrey E Hinton, Simon Osindero, Yee-Whye Teh. Neural Comput 2006
8


Deep supervised, but not unsupervised, models may explain IT cortical representation.
Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
346
7


Using goal-driven deep learning models to understand sensory cortex.
Daniel L K Yamins, James J DiCarlo. Nat Neurosci 2016
330
6


Deep Learning in Medical Image Analysis.
Dinggang Shen, Guorong Wu, Heung-Il Suk. Annu Rev Biomed Eng 2017
727
5

A deep learning framework for neuroscience.
Blake A Richards, Timothy P Lillicrap, Philippe Beaudoin, Yoshua Bengio, Rafal Bogacz, Amelia Christensen, Claudia Clopath, Rui Ponte Costa, Archy de Berker, Surya Ganguli,[...]. Nat Neurosci 2019
108
5

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.
Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun. IEEE Trans Pattern Anal Mach Intell 2017
935
5

Image reconstruction by domain-transform manifold learning.
Bo Zhu, Jeremiah Z Liu, Stephen F Cauley, Bruce R Rosen, Matthew S Rosen. Nature 2018
295
4

Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.
Courtney J Spoerer, Patrick McClure, Nikolaus Kriegeskorte. Front Psychol 2017
40
10


Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks.
Rishi Rajalingham, Elias B Issa, Pouya Bashivan, Kohitij Kar, Kailyn Schmidt, James J DiCarlo. J Neurosci 2018
76
5


Building machines that learn and think like people.
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, Samuel J Gershman. Behav Brain Sci 2017
157
4

Neural population control via deep image synthesis.
Pouya Bashivan, Kohitij Kar, James J DiCarlo. Science 2019
67
5

Seeing it all: Convolutional network layers map the function of the human visual system.
Michael Eickenberg, Alexandre Gramfort, Gaël Varoquaux, Bertrand Thirion. Neuroimage 2017
66
6

Brain tumor segmentation with Deep Neural Networks.
Mohammad Havaei, Axel Davy, David Warde-Farley, Antoine Biard, Aaron Courville, Yoshua Bengio, Chris Pal, Pierre-Marc Jodoin, Hugo Larochelle. Med Image Anal 2017
459
4

Random synaptic feedback weights support error backpropagation for deep learning.
Timothy P Lillicrap, Daniel Cownden, Douglas B Tweed, Colin J Akerman. Nat Commun 2016
112
4

Deep neural networks rival the representation of primate IT cortex for core visual object recognition.
Charles F Cadieu, Ha Hong, Daniel L K Yamins, Nicolas Pinto, Diego Ardila, Ethan A Solomon, Najib J Majaj, James J DiCarlo. PLoS Comput Biol 2014
190
4

Canonical microcircuits for predictive coding.
Andre M Bastos, W Martin Usrey, Rick A Adams, George R Mangun, Pascal Fries, Karl J Friston. Neuron 2012
899
4


Artificial convolution neural network techniques and applications for lung nodule detection.
S B Lo, S A Lou, J S Lin, M T Freedman, M V Chien, S K Mun. IEEE Trans Med Imaging 1995
77
5


Neuroscience-Inspired Artificial Intelligence.
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, Matthew Botvinick. Neuron 2017
174
4


SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.
Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla. IEEE Trans Pattern Anal Mach Intell 2017
734
4

Improved protein structure prediction using potentials from deep learning.
Andrew W Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander W R Nelson, Alex Bridgland,[...]. Nature 2020
445
4

Slow insertion of silicon probes improves the quality of acute neuronal recordings.
Richárd Fiáth, Adrienn Lilla Márton, Ferenc Mátyás, Domonkos Pinke, Gergely Márton, Kinga Tóth, István Ulbert. Sci Rep 2019
18
16

Deep Learning in Medical Imaging: General Overview.
June-Goo Lee, Sanghoon Jun, Young-Won Cho, Hyunna Lee, Guk Bae Kim, Joon Beom Seo, Namkug Kim. Korean J Radiol 2017
229
3

Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Nima Tajbakhsh, Jae Y Shin, Suryakanth R Gurudu, R Todd Hurst, Christopher B Kendall, Michael B Gotway, Jianming Liang. IEEE Trans Med Imaging 2016
448
3

Overview of deep learning in medical imaging.
Kenji Suzuki. Radiol Phys Technol 2017
169
3

Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images.
B Sahiner, H P Chan, N Petrick, D Wei, M A Helvie, D D Adler, M M Goodsitt. IEEE Trans Med Imaging 1996
121
3


Artificial intelligence in radiology.
Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H Schwartz, Hugo J W L Aerts. Nat Rev Cancer 2018
501
3

Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network.
Fangzhou Liao, Ming Liang, Zhe Li, Xiaolin Hu, Sen Song. IEEE Trans Neural Netw Learn Syst 2019
57
5

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography.
Diego Ardila, Atilla P Kiraly, Sujeeth Bharadwaj, Bokyung Choi, Joshua J Reicher, Lily Peng, Daniel Tse, Mozziyar Etemadi, Wenxing Ye, Greg Corrado,[...]. Nat Med 2019
327
3

Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks.
Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Geert Litjens, Paul Gerke, Colin Jacobs, Sarah J van Riel, Mathilde Marie Winkler Wille, Matiullah Naqibullah, Clara I Sanchez, Bram van Ginneken. IEEE Trans Med Imaging 2016
254
3


Co-cited is the co-citation frequency, indicating how many articles cite the article together with the query article. Similarity is the co-citation as percentage of the times cited of the query article or the article in the search results, whichever is the lowest. These numbers are calculated for the last 100 citations when articles are cited more than 100 times.