A citation-based method for searching scientific literature

Chi Zhang, Kai Qiao, Linyuan Wang, Li Tong, Guoen Hu, Ru-Yuan Zhang, Bin Yan. J Neurosci Methods 2019
Times Cited: 7







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



Times Cited
  Times     Co-cited
Similarity



Bayesian reconstruction of natural images from human brain activity.
Thomas Naselaris, Ryan J Prenger, Kendrick N Kay, Michael Oliver, Jack L Gallant. Neuron 2009
219
42

Identifying natural images from human brain activity.
Kendrick N Kay, Thomas Naselaris, Ryan J Prenger, Jack L Gallant. Nature 2008
520
42


Deep learning in neural networks: an overview.
Jürgen Schmidhuber. Neural Netw 2015
28

Encoding and decoding in fMRI.
Thomas Naselaris, Kendrick N Kay, Shinji Nishimoto, Jack L Gallant. Neuroimage 2011
296
28

Predicting human brain activity associated with the meanings of nouns.
Tom M Mitchell, Svetlana V Shinkareva, Andrew Carlson, Kai-Min Chang, Vicente L Malave, Robert A Mason, Marcel Adam Just. Science 2008
408
28


Seeing it all: Convolutional network layers map the function of the human visual system.
Michael Eickenberg, Alexandre Gramfort, Gaël Varoquaux, Bertrand Thirion. Neuroimage 2017
61
28




Deep Learning: The Good, the Bad, and the Ugly.
Thomas Serre. Annu Rev Vis Sci 2019
28
28



Using goal-driven deep learning models to understand sensory cortex.
Daniel L K Yamins, James J DiCarlo. Nat Neurosci 2016
314
28


Multimodal Classification of Schizophrenia Patients with MEG and fMRI Data Using Static and Dynamic Connectivity Measures.
Mustafa S Cetin, Jon M Houck, Barnaly Rashid, Oktay Agacoglu, Julia M Stephen, Jing Sui, Jose Canive, Andy Mayer, Cheryl Aine, Juan R Bustillo,[...]. Front Neurosci 2016
34
14



Transfer learning of deep neural network representations for fMRI decoding.
Michele Svanera, Mattia Savardi, Sergio Benini, Alberto Signoroni, Gal Raz, Talma Hendler, Lars Muckli, Rainer Goebel, Giancarlo Valente. J Neurosci Methods 2019
2
50

Multi-Hypergraph Learning for Incomplete Multimodality Data.
Mingxia Liu, Yue Gao, Pew-Thian Yap, Dinggang Shen. IEEE J Biomed Health Inform 2018
4
25

Early prediction of epileptic seizures using a long-term recurrent convolutional network.
Xiaoyan Wei, Lin Zhou, Zhen Zhang, Ziyi Chen, Yi Zhou. J Neurosci Methods 2019
13
14

Decentralized distribution-sampled classification models with application to brain imaging.
Noah Lewis, Harshvardhan Gazula, Sergey M Plis, Vince D Calhoun. J Neurosci Methods 2020
4
25


DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI.
Atif Riaz, Muhammad Asad, Eduardo Alonso, Greg Slabaugh. J Neurosci Methods 2020
8
14

Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion.
Jing Sui, Shile Qi, Theo G M van Erp, Juan Bustillo, Rongtao Jiang, Dongdong Lin, Jessica A Turner, Eswar Damaraju, Andrew R Mayer, Yue Cui,[...]. Nat Commun 2018
53
14


Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data.
Weizheng Yan, Vince Calhoun, Ming Song, Yue Cui, Hao Yan, Shengfeng Liu, Lingzhong Fan, Nianming Zuo, Zhengyi Yang, Kaibin Xu,[...]. EBioMedicine 2019
18
14

Safe Classification with Augmented Features.
Chenping Hou, Ling-Li Zeng, Dewen Hu. IEEE Trans Pattern Anal Mach Intell 2019
2
50

Deep convolutional neural network for classification of sleep stages from single-channel EEG signals.
Z Mousavi, T Yousefi Rezaii, S Sheykhivand, A Farzamnia, S N Razavi. J Neurosci Methods 2019
14
14

Measurement-oriented deep-learning workflow for improved segmentation of myelin and axons in high-resolution images of human cerebral white matter.
Predrag Janjic, Kristijan Petrovski, Blagoja Dolgoski, John Smiley, Panche Zdravkovski, Goran Pavlovski, Zlatko Jakjovski, Natasa Davceva, Verica Poposka, Aleksandar Stankov,[...]. J Neurosci Methods 2019
4
25

Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness.
Vince D Calhoun, Jing Sui. Biol Psychiatry Cogn Neurosci Neuroimaging 2016
127
14


Computer-assisted enhanced volumetric segmentation magnetic resonance imaging data using a mixture of artificial neural networks.
Rigoberto Pérez de Alejo, Jesús Ruiz-Cabello, Manuel Cortijo, Ignacio Rodriguez, Imanol Echave, Javier Regadera, Juan Arrazola, Pablo Avilés, Pilar Barreiro, Domingo Gargallo,[...]. Magn Reson Imaging 2003
14
14

Generalized core vector machines.
Ivor Wai-Hung Tsang, James Tin-Yau Kwok, Jacek M Zurada. IEEE Trans Neural Netw 2006
22
14


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
432
14

Co-Saliency Detection via a Self-Paced Multiple-Instance Learning Framework.
Dingwen Zhang, Deyu Meng, Junwei Han. IEEE Trans Pattern Anal Mach Intell 2017
29
14

Tumour volume determination from MR images by morphological segmentation.
P Gibbs, D L Buckley, S J Blackband, A Horsman. Phys Med Biol 1996
47
14

Automatic brain tissue segmentation in fetal MRI using convolutional neural networks.
N Khalili, N Lessmann, E Turk, N Claessens, R de Heus, T Kolk, M A Viergever, M J N L Benders, I Išgum. Magn Reson Imaging 2019
18
14

Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.
Sergio Pereira, Adriano Pinto, Victor Alves, Carlos A Silva. IEEE Trans Med Imaging 2016
361
14


Learning image-based spatial transformations via convolutional neural networks: A review.
Nicholas J Tustison, Brian B Avants, James C Gee. Magn Reson Imaging 2019
7
14

Conventional MRI evaluation of gliomas.
N Upadhyay, A D Waldman. Br J Radiol 2011
92
14



Review of neural network applications in medical imaging and signal processing.
A S Miller, B H Blott, T K Hames. Med Biol Eng Comput 1992
106
14

State of the art survey on MRI brain tumor segmentation.
Nelly Gordillo, Eduard Montseny, Pilar Sobrevilla. Magn Reson Imaging 2013
115
14

Neural network-based segmentation of dynamic MR mammographic images.
Robert Lucht, Stefan Delorme, Gunnar Brix. Magn Reson Imaging 2002
29
14


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.