Gabriel Chartrand, Phillip M Cheng, Eugene Vorontsov, Michal Drozdzal, Simon Turcotte, Christopher J Pal, Samuel Kadoury, An Tang. Radiographics 2017
Times Cited: 438
Times Cited: 438
Times Cited
Times Co-cited
Similarity
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
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
17
Radiomics: Images Are More than Pictures, They Are Data.
Robert J Gillies, Paul E Kinahan, Hedvig Hricak. Radiology 2016
Robert J Gillies, Paul E Kinahan, Hedvig Hricak. Radiology 2016
12
Artificial intelligence in radiology.
Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H Schwartz, Hugo J W L Aerts. Nat Rev Cancer 2018
Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H Schwartz, Hugo J W L Aerts. Nat Rev Cancer 2018
11
Radiomics: the bridge between medical imaging and personalized medicine.
Philippe Lambin, Ralph T H Leijenaar, Timo M Deist, Jurgen Peerlings, Evelyn E C de Jong, Janita van Timmeren, Sebastian Sanduleanu, Ruben T H M Larue, Aniek J G Even, Arthur Jochems,[...]. Nat Rev Clin Oncol 2017
Philippe Lambin, Ralph T H Leijenaar, Timo M Deist, Jurgen Peerlings, Evelyn E C de Jong, Janita van Timmeren, Sebastian Sanduleanu, Ruben T H M Larue, Aniek J G Even, Arthur Jochems,[...]. Nat Rev Clin Oncol 2017
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Machine Learning for Medical Imaging.
Bradley J Erickson, Panagiotis Korfiatis, Zeynettin Akkus, Timothy L Kline. Radiographics 2017
Bradley J Erickson, Panagiotis Korfiatis, Zeynettin Akkus, Timothy L Kline. Radiographics 2017
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High-performance medicine: the convergence of human and artificial intelligence.
Eric J Topol. Nat Med 2019
Eric J Topol. Nat Med 2019
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International evaluation of an AI system for breast cancer screening.
Scott Mayer McKinney, Marcin Sieniek, Varun Godbole, Jonathan Godwin, Natasha Antropova, Hutan Ashrafian, Trevor Back, Mary Chesus, Greg S Corrado, Ara Darzi,[...]. Nature 2020
Scott Mayer McKinney, Marcin Sieniek, Varun Godbole, Jonathan Godwin, Natasha Antropova, Hutan Ashrafian, Trevor Back, Mary Chesus, Greg S Corrado, Ara Darzi,[...]. Nature 2020
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Liver Fibrosis: Deep Convolutional Neural Network for Staging by Using Gadoxetic Acid-enhanced Hepatobiliary Phase MR Images.
Koichiro Yasaka, Hiroyuki Akai, Akira Kunimatsu, Osamu Abe, Shigeru Kiryu. Radiology 2018
Koichiro Yasaka, Hiroyuki Akai, Akira Kunimatsu, Osamu Abe, Shigeru Kiryu. Radiology 2018
7
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
Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, Sebastian Thrun. Nature 2017
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Deep Learning: An Update for Radiologists.
Phillip M Cheng, Emmanuel Montagnon, Rikiya Yamashita, Ian Pan, Alexandre Cadrin-Chênevert, Francisco Perdigón Romero, Gabriel Chartrand, Samuel Kadoury, An Tang. Radiographics 2021
Phillip M Cheng, Emmanuel Montagnon, Rikiya Yamashita, Ian Pan, Alexandre Cadrin-Chênevert, Francisco Perdigón Romero, Gabriel Chartrand, Samuel Kadoury, An Tang. Radiographics 2021
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Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study.
Koichiro Yasaka, Hiroyuki Akai, Osamu Abe, Shigeru Kiryu. Radiology 2018
Koichiro Yasaka, Hiroyuki Akai, Osamu Abe, Shigeru Kiryu. Radiology 2018
7
Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers.
John Mongan, Linda Moy, Charles E Kahn. Radiol Artif Intell 2020
John Mongan, Linda Moy, Charles E Kahn. Radiol Artif Intell 2020
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Improvement of image quality at CT and MRI using deep learning.
Toru Higaki, Yuko Nakamura, Fuminari Tatsugami, Takeshi Nakaura, Kazuo Awai. Jpn J Radiol 2019
Toru Higaki, Yuko Nakamura, Fuminari Tatsugami, Takeshi Nakaura, Kazuo Awai. Jpn J Radiol 2019
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Current Applications and Future Impact of Machine Learning in Radiology.
Garry Choy, Omid Khalilzadeh, Mark Michalski, Synho Do, Anthony E Samir, Oleg S Pianykh, J Raymond Geis, Pari V Pandharipande, James A Brink, Keith J Dreyer. Radiology 2018
Garry Choy, Omid Khalilzadeh, Mark Michalski, Synho Do, Anthony E Samir, Oleg S Pianykh, J Raymond Geis, Pari V Pandharipande, James A Brink, Keith J Dreyer. Radiology 2018
6
Deep Learning in Medical Image Analysis.
Dinggang Shen, Guorong Wu, Heung-Il Suk. Annu Rev Biomed Eng 2017
Dinggang Shen, Guorong Wu, Heung-Il Suk. Annu Rev Biomed Eng 2017
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Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.
Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros,[...]. JAMA 2016
Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros,[...]. JAMA 2016
5
Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI.
Charlie A Hamm, Clinton J Wang, Lynn J Savic, Marc Ferrante, Isabel Schobert, Todd Schlachter, MingDe Lin, James S Duncan, Jeffrey C Weinreb, Julius Chapiro,[...]. Eur Radiol 2019
Charlie A Hamm, Clinton J Wang, Lynn J Savic, Marc Ferrante, Isabel Schobert, Todd Schlachter, MingDe Lin, James S Duncan, Jeffrey C Weinreb, Julius Chapiro,[...]. Eur Radiol 2019
5
Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study.
John R Zech, Marcus A Badgeley, Manway Liu, Anthony B Costa, Joseph J Titano, Eric Karl Oermann. PLoS Med 2018
John R Zech, Marcus A Badgeley, Manway Liu, Anthony B Costa, Joseph J Titano, Eric Karl Oermann. PLoS Med 2018
5
An overview of deep learning in medical imaging focusing on MRI.
Alexander Selvikvåg Lundervold, Arvid Lundervold. Z Med Phys 2019
Alexander Selvikvåg Lundervold, Arvid Lundervold. Z Med Phys 2019
5
Convolutional neural networks: an overview and application in radiology.
Rikiya Yamashita, Mizuho Nishio, Richard Kinh Gian Do, Kaori Togashi. Insights Imaging 2018
Rikiya Yamashita, Mizuho Nishio, Richard Kinh Gian Do, Kaori Togashi. Insights Imaging 2018
5
Deep learning with convolutional neural network in radiology.
Koichiro Yasaka, Hiroyuki Akai, Akira Kunimatsu, Shigeru Kiryu, Osamu Abe. Jpn J Radiol 2018
Koichiro Yasaka, Hiroyuki Akai, Akira Kunimatsu, Shigeru Kiryu, Osamu Abe. Jpn J Radiol 2018
5
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).
Bjoern H Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom, Roland Wiest,[...]. IEEE Trans Med Imaging 2015
Bjoern H Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom, Roland Wiest,[...]. IEEE Trans Med Imaging 2015
5
A guide to deep learning in healthcare.
Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov, Mark DePristo, Katherine Chou, Claire Cui, Greg Corrado, Sebastian Thrun, Jeff Dean. Nat Med 2019
Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov, Mark DePristo, Katherine Chou, Claire Cui, Greg Corrado, Sebastian Thrun, Jeff Dean. Nat Med 2019
5
Reporting of artificial intelligence prediction models.
Gary S Collins, Karel G M Moons. Lancet 2019
Gary S Collins, Karel G M Moons. Lancet 2019
4
The RSNA Pediatric Bone Age Machine Learning Challenge.
Safwan S Halabi, Luciano M Prevedello, Jayashree Kalpathy-Cramer, Artem B Mamonov, Alexander Bilbily, Mark Cicero, Ian Pan, Lucas Araújo Pereira, Rafael Teixeira Sousa, Nitamar Abdala,[...]. Radiology 2019
Safwan S Halabi, Luciano M Prevedello, Jayashree Kalpathy-Cramer, Artem B Mamonov, Alexander Bilbily, Mark Cicero, Ian Pan, Lucas Araújo Pereira, Rafael Teixeira Sousa, Nitamar Abdala,[...]. Radiology 2019
4
Deep Learning for Accurate Diagnosis of Liver Tumor Based on Magnetic Resonance Imaging and Clinical Data.
Shi-Hui Zhen, Ming Cheng, Yu-Bo Tao, Yi-Fan Wang, Sarun Juengpanich, Zhi-Yu Jiang, Yan-Kai Jiang, Yu-Yu Yan, Wei Lu, Jie-Min Lue,[...]. Front Oncol 2020
Shi-Hui Zhen, Ming Cheng, Yu-Bo Tao, Yi-Fan Wang, Sarun Juengpanich, Zhi-Yu Jiang, Yan-Kai Jiang, Yu-Yu Yan, Wei Lu, Jie-Min Lue,[...]. Front Oncol 2020
9
Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study.
Michael P Recht, Jure Zbontar, Daniel K Sodickson, Florian Knoll, Nafissa Yakubova, Anuroop Sriram, Tullie Murrell, Aaron Defazio, Michael Rabbat, Leon Rybak,[...]. AJR Am J Roentgenol 2020
Michael P Recht, Jure Zbontar, Daniel K Sodickson, Florian Knoll, Nafissa Yakubova, Anuroop Sriram, Tullie Murrell, Aaron Defazio, Michael Rabbat, Leon Rybak,[...]. AJR Am J Roentgenol 2020
8
Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology.
An Tang, Roger Tam, Alexandre Cadrin-Chênevert, Will Guest, Jaron Chong, Joseph Barfett, Leonid Chepelev, Robyn Cairns, J Ross Mitchell, Mark D Cicero,[...]. Can Assoc Radiol J 2018
An Tang, Roger Tam, Alexandre Cadrin-Chênevert, Will Guest, Jaron Chong, Joseph Barfett, Leonid Chepelev, Robyn Cairns, J Ross Mitchell, Mark D Cicero,[...]. Can Assoc Radiol J 2018
4
A Deep Learning Model to Triage Screening Mammograms: A Simulation Study.
Adam Yala, Tal Schuster, Randy Miles, Regina Barzilay, Constance Lehman. Radiology 2019
Adam Yala, Tal Schuster, Randy Miles, Regina Barzilay, Constance Lehman. Radiology 2019
5
The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database.
Stan Benjamens, Pranavsingh Dhunnoo, Bertalan Meskó. NPJ Digit Med 2020
Stan Benjamens, Pranavsingh Dhunnoo, Bertalan Meskó. NPJ Digit Med 2020
4
Low-dose CT via convolutional neural network.
Hu Chen, Yi Zhang, Weihua Zhang, Peixi Liao, Ke Li, Jiliu Zhou, Ge Wang. Biomed Opt Express 2017
Hu Chen, Yi Zhang, Weihua Zhang, Peixi Liao, Ke Li, Jiliu Zhou, Ge Wang. Biomed Opt Express 2017
4
Sparse MRI: The application of compressed sensing for rapid MR imaging.
Michael Lustig, David Donoho, John M Pauly. Magn Reson Med 2007
Michael Lustig, David Donoho, John M Pauly. Magn Reson Med 2007
4
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.
Hugo J W L Aerts, Emmanuel Rios Velazquez, Ralph T H Leijenaar, Chintan Parmar, Patrick Grossmann, Sara Carvalho, Johan Bussink, René Monshouwer, Benjamin Haibe-Kains, Derek Rietveld,[...]. Nat Commun 2014
Hugo J W L Aerts, Emmanuel Rios Velazquez, Ralph T H Leijenaar, Chintan Parmar, Patrick Grossmann, Sara Carvalho, Johan Bussink, René Monshouwer, Benjamin Haibe-Kains, Derek Rietveld,[...]. Nat Commun 2014
4
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping.
Alex Zwanenburg, Martin Vallières, Mahmoud A Abdalah, Hugo J W L Aerts, Vincent Andrearczyk, Aditya Apte, Saeed Ashrafinia, Spyridon Bakas, Roelof J Beukinga, Ronald Boellaard,[...]. Radiology 2020
Alex Zwanenburg, Martin Vallières, Mahmoud A Abdalah, Hugo J W L Aerts, Vincent Andrearczyk, Aditya Apte, Saeed Ashrafinia, Spyridon Bakas, Roelof J Beukinga, Ronald Boellaard,[...]. Radiology 2020
4
The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository.
Kenneth Clark, Bruce Vendt, Kirk Smith, John Freymann, Justin Kirby, Paul Koppel, Stephen Moore, Stanley Phillips, David Maffitt, Michael Pringle,[...]. J Digit Imaging 2013
Kenneth Clark, Bruce Vendt, Kirk Smith, John Freymann, Justin Kirby, Paul Koppel, Stephen Moore, Stanley Phillips, David Maffitt, Michael Pringle,[...]. J Digit Imaging 2013
4
Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction.
Seong Ho Park, Kyunghwa Han. Radiology 2018
Seong Ho Park, Kyunghwa Han. Radiology 2018
4
Deep Learning for Predicting Enhancing Lesions in Multiple Sclerosis from Noncontrast MRI.
Ponnada A Narayana, Ivan Coronado, Sheeba J Sujit, Jerry S Wolinsky, Fred D Lublin, Refaat E Gabr. Radiology 2020
Ponnada A Narayana, Ivan Coronado, Sheeba J Sujit, Jerry S Wolinsky, Fred D Lublin, Refaat E Gabr. Radiology 2020
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Preparing Medical Imaging Data for Machine Learning.
Martin J Willemink, Wojciech A Koszek, Cailin Hardell, Jie Wu, Dominik Fleischmann, Hugh Harvey, Les R Folio, Ronald M Summers, Daniel L Rubin, Matthew P Lungren. Radiology 2020
Martin J Willemink, Wojciech A Koszek, Cailin Hardell, Jie Wu, Dominik Fleischmann, Hugh Harvey, Les R Folio, Ronald M Summers, Daniel L Rubin, Matthew P Lungren. Radiology 2020
4
Pilot study: Application of artificial intelligence for detecting left atrial enlargement on canine thoracic radiographs.
Shen Li, Zigui Wang, Lance C Visser, Erik R Wisner, Hao Cheng. Vet Radiol Ultrasound 2020
Shen Li, Zigui Wang, Lance C Visser, Erik R Wisner, Hao Cheng. Vet Radiol Ultrasound 2020
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The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review.
Hugo J W L Aerts. JAMA Oncol 2016
Hugo J W L Aerts. JAMA Oncol 2016
4
The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload.
Robert J McDonald, Kara M Schwartz, Laurence J Eckel, Felix E Diehn, Christopher H Hunt, Brian J Bartholmai, Bradley J Erickson, David F Kallmes. Acad Radiol 2015
Robert J McDonald, Kara M Schwartz, Laurence J Eckel, Felix E Diehn, Christopher H Hunt, Brian J Bartholmai, Bradley J Erickson, David F Kallmes. Acad Radiol 2015
4
Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks.
Paras Lakhani, Baskaran Sundaram. Radiology 2017
Paras Lakhani, Baskaran Sundaram. Radiology 2017
4
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
June-Goo Lee, Sanghoon Jun, Young-Won Cho, Hyunna Lee, Guk Bae Kim, Joon Beom Seo, Namkug Kim. Korean J Radiol 2017
4
The practical implementation of artificial intelligence technologies in medicine.
Jianxing He, Sally L Baxter, Jie Xu, Jiming Xu, Xingtao Zhou, Kang Zhang. Nat Med 2019
Jianxing He, Sally L Baxter, Jie Xu, Jiming Xu, Xingtao Zhou, Kang Zhang. Nat Med 2019
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Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection.
Constance D Lehman, Robert D Wellman, Diana S M Buist, Karla Kerlikowske, Anna N A Tosteson, Diana L Miglioretti. JAMA Intern Med 2015
Constance D Lehman, Robert D Wellman, Diana S M Buist, Karla Kerlikowske, Anna N A Tosteson, Diana L Miglioretti. JAMA Intern Med 2015
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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
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
3
Convolutional Neural Networks for Radiologic Images: A Radiologist's Guide.
Shelly Soffer, Avi Ben-Cohen, Orit Shimon, Michal Marianne Amitai, Hayit Greenspan, Eyal Klang. Radiology 2019
Shelly Soffer, Avi Ben-Cohen, Orit Shimon, Michal Marianne Amitai, Hayit Greenspan, Eyal Klang. Radiology 2019
3
Current applications and future directions of deep learning in musculoskeletal radiology.
Pauley Chea, Jacob C Mandell. Skeletal Radiol 2020
Pauley Chea, Jacob C Mandell. Skeletal Radiol 2020
7
Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans.
Naofumi Tomita, Yvonne Y Cheung, Saeed Hassanpour. Comput Biol Med 2018
Naofumi Tomita, Yvonne Y Cheung, Saeed Hassanpour. Comput Biol Med 2018
4
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.