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Daniel L. Rubin

Active 1992–2026

342
Papers
26,554
Citations
80
h-index
249
i10-index

Citations

Citations per year for Daniel L. Rubin1983: 1 citations1986: 1 citations1991: 3 citations1994: 1 citations1995: 1 citations1996: 3 citations1997: 4 citations1998: 1 citations1999: 3 citations2000: 7 citations2001: 8 citations2002: 14 citations2003: 17 citations2004: 38 citations2005: 47 citations2006: 45 citations2007: 58 citations2008: 106 citations2009: 174 citations2010: 227 citations2011: 282 citations2012: 345 citations2013: 353 citations2014: 367 citations2015: 352 citations2016: 425 citations2017: 544 citations2018: 667 citations2019: 1,256 citations2020: 1,394 citations2021: 1,611 citations2022: 1,563 citations2023: 1,496 citations2024: 1,702 citations2025: 1,101 citations2026: 193 citations1984–1985: no citations, so these years are not shown1987–1990: no citations, so these years are not shown1992–1993: no citations, so these years are not shown

Citation sources

Countries

World map of the countries and regions citing this authorUnited States: 4,503 citing papers, 24.2% of this breakdownChina: 2,646 citing papers, 14.2% of this breakdownUnited Kingdom: 1,125 citing papers, 6.1% of this breakdownGermany: 845 citing papers, 4.5% of this breakdownIndia: 745 citing papers, 4% of this breakdownCanada: 655 citing papers, 3.5% of this breakdownItaly: 574 citing papers, 3.1% of this breakdownFrance: 527 citing papers, 2.8% of this breakdownAustralia: 474 citing papers, 2.6% of this breakdownNetherlands: 457 citing papers, 2.5% of this breakdownSouth Korea: 453 citing papers, 2.4% of this breakdownSpain: 379 citing papers, 2% of this breakdown
0%24.2%Other 28.1%

Institutions

Fields

  • Medicine39%
  • Computer Science32.1%
  • Biochemistry, Genetics and Molecular Biology13%
  • Neuroscience8%
  • Engineering2.6%
  • Health Professions1%
  • Other4.3%

Topics

  • Radiomics and Machine Learning in Medical Imaging9.2%
  • AI in cancer detection8.2%
  • Biomedical Text Mining and Ontologies4.3%
  • Artificial Intelligence in Healthcare and Education3%
  • Semantic Web and Ontologies2.6%
  • COVID-19 diagnosis using AI2.4%
  • Other70.3%

Coauthors

All papers

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  1. A curated mammography data set for use in computer-aided detection and diagnosis research

    Authors: Rebecca Sawyer Lee, Francisco Javier Giménez Fuentes‐Guerra, Assaf Hoogi, Kanae K. Miyake, Mia Gorovoy, Daniel L. Rubin - Scientific Data 2017 cited by 796

  2. Preparing Medical Imaging Data for Machine Learning

    Authors: Martin J. Willemink, Wojciech A. Koszek, Cailin Hardell, Jie Wu, Dominik Fleischmann, Hugh Harvey, Les Folio, Ronald M. Summers, Daniel L. Rubin, Matthew P. Lungren - Radiology 2020 cited by 976

  3. A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions

    Authors: Sergios Gatidis, Tobias Hepp, Marcel Früh, Christian la Fougère, Konstantin Nikolaou, Christina Pfannenberg, Bernhard Schölkopf, Thomas Küstner, Clemens C. Cyran, Daniel L. Rubin - Scientific Data 2022 cited by 248

  4. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

    Authors: Zeynettin Akkus, Alfiia Galimzianova, Assaf Hoogi, Daniel L. Rubin, Bradley J. Erickson - Journal of Digital Imaging, J. Digit. Imaging 2017 cited by 1,109

  5. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

    Authors: Kun‐Hsing Yu, Ce Zhang, Gerald J. Berry, Russ B. Altman, Christopher Ré, Daniel L. Rubin, M Snyder - Nature Communications 2016 cited by 1,013

  6. Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging

    Authors: Rui Yan, Liangqiong Qu, Qingyue Wei, Shih-Cheng Huang, Liyue Shen, Daniel L. Rubin, Lei Xing, Yuyin Zhou - IEEE Transactions on Medical Imaging, IEEE Trans. Medical Imaging 2023 cited by 164

  7. Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study

    Authors: Rikiya Yamashita, Jin Long, Teri A. Longacre, Lan Peng, Gerald J. Berry, Brock A. Martin, John Higgins, Daniel L. Rubin, Jeanne Shen - The Lancet Oncology 2020 cited by 379

  8. Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

    Authors: Thomas Schaffter, Diana S.M. Buist, Christoph I. Lee, Yaroslav Nikulin, Dezső Ribli, Yuanfang Guan, William Lotter, Zequn Jie, Hao Du, Sijia Wang, Jiashi Feng, Mengling Feng, Hyoeun Kim, F. Albiol, Alberto Albiol, Stephen Morrell, Zbigniew Wojna, Mehmet Eren Ahsen, Umar Asif, Antonio Jimeno Yepes, Shivanthan A.C. Yohanandan, Simona Rabinovici‐Cohen, Darvin Yi, Bruce Hoff, Thomas Yu, Elias Chaibub Neto, Daniel L. Rubin, Peter Lindholm, Laurie R. Margolies, Russell B. McBride, Joseph H. Rothstein, Weiva Sieh, Rami Ben‐Ari, Stefan Harrer, Andrew D. Trister, Stephen Friend, Thea Norman, Berkman Sahiner, Fredrik Strand, Justin Guinney, Gustavo Stolovitzky, Lester Mackey, Joyce Cahoon, Li Shen, Jae Ho Sohn, Hari Trivedi, Yiqiu Shen, Ljubomir Buturović, José Costa Pereira, Jaime S. Cardoso, Eduardo Castro, Karl Trygve Kalleberg, Obioma Pelka, Imane Nedjar, Krzysztof J. Geras, Felix Nensa, Ethan Goan, Sven Koitka, L. Caballero, David Cox, Pavitra Krishnaswamy, Gaurav Pandey, Christoph M. Friedrich, Dimitri Perrin, Clinton Fookes, Bibo Shi, Gerard Cardoso Negrie, Michael Kawczynski, Kyunghyun Cho, Can Son Khoo, Joseph Y. Lo, A. Gregory Sorensen, Hwejin Jung - JAMA Network Open 2020 cited by 414

  9. A radiogenomic dataset of non-small cell lung cancer

    Authors: Shaimaa Bakr, Olivier Gevaert, Sebastian Echegaray, Kelsey Ayers, Mu Zhou, Mājid Shafiq, Hong Zheng, Jalen Benson, Weiruo Zhang, Ann N. Leung, Michel Kadoch, Chuong D. Hoang, Joseph B. Shrager, Andrew Quon, Daniel L. Rubin, Sylvia K. Plevritis, Sandy Napel - Scientific Data 2018 cited by 315

  10. CT-ORG, a new dataset for multiple organ segmentation in computed tomography

    Authors: Blaine Rister, Darvin Yi, Kaushik Shivakumar, Tomomi W. Nobashi, Daniel L. Rubin - Scientific Data 2020 cited by 142

  11. Distributed deep learning networks among institutions for medical imaging

    Authors: Ken Chang, Niranjan Balachandar, Carson K. Lam, Darvin Yi, James M. Brown, Andrew Beers, Bruce R. Rosen, Daniel L. Rubin, Jayashree Kalpathy-Cramer - Journal of the American Medical Informatics Association, J. Am. Medical Informatics Assoc. 2018 cited by 345

  12. Privacy preservation for federated learning in health care

    Authors: Sarthak Pati, Sourav Kumar, Amokh Varma, Brandon Edwards, Charles Lu, Liangqiong Qu, Justin J. Wang, Anantharaman Lakshminarayanan, Shih-han Wang, Micah J. Sheller, Ken Chang, Praveer Singh, Daniel L. Rubin, Jayashree Kalpathy-Cramer, Spyridon Bakas - Patterns 2024 cited by 142

  13. Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant Chemotherapy

    Authors: Jia Wu, Guohong Cao, Xiaoli Sun, Juheon Lee, Daniel L. Rubin, Sandy Napel, Allison W. Kurian, Bruce L. Daniel, Ruijiang Li - Radiology 2018 cited by 209

  14. SplitAVG: A Heterogeneity-Aware Federated Deep Learning Method for Medical Imaging

    Authors: Miao Zhang, Liangqiong Qu, Praveer Singh, Jayashree Kalpathy-Cramer, Daniel L. Rubin - IEEE Journal of Biomedical and Health Informatics, IEEE J. Biomed. Health Informatics 2022 cited by 79

  15. Regulatory Frameworks for Development and Evaluation of Artificial Intelligence–Based Diagnostic Imaging Algorithms: Summary and Recommendations

    Authors: David B. Larson, Hugh Harvey, Daniel L. Rubin, Neville Irani, Justin R. Tse, Curtis P. Langlotz - Journal of the American College of Radiology 2020 cited by 184

  16. Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification

    Authors: Imon Banerjee, Ling Yuan, Matthew C. Chen, Sadid A. Hasan, Curtis P. Langlotz, N Moradzadeh, Brian E. Chapman, Timothy J. Amrhein, David A. Mong, Daniel L. Rubin, Oladimeji Farri, Matthew P. Lungren - Artificial Intelligence in Medicine, Artif. Intell. Medicine 2018 cited by 280

  17. Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence

    Authors: Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan, Ziwei Fan, Fan Yang, Ke Ma, Jiehua Yang, Song Bai, Chang Shu, Xinyu Zou, Renhao Huang, Changzheng Zhang, Xiaowu Liu, Dandan Tu, Chuou Xu, Wenqing Zhang, Xi Wang, Anguo Chen, Yu Zeng, Dehua Yang, Ming-Wei Wang, Nagaraj Holalkere, Neil J. Halin, Ihab R. Kamel, Jia Wu, Xuehua Peng, Xiang Wang, Jianbo Shao, Pattanasak Mongkolwat, Jianjun Zhang, Weiyang Liu, Michael Roberts, Zhongzhao Teng, Lucian Beer, Lorena Escudero Sanchez, Evis Sala, Daniel L. Rubin, Adrian Weller, Joan Lasenby, Chuansheng Zheng, Jianming Wang, Zhen Li, Carola Schönlieb, Tian Xia - Nature Machine Intelligence, Nat. Mach. Intell. 2021 cited by 76

  18. Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease

    Authors: Kaustubh Supekar, Vinod Menon, Daniel L. Rubin, Mark A. Musen, Michael D. Greicius - PLoS Computational Biology, PLoS Comput. Biol. 2008 cited by 1,170

  19. Federated Learning for Breast Density Classification: A Real-World Implementation

    Authors: Holger R. Roth, Ken Chang, Praveer Singh, Nir Neumark, Wenqi Li, Vikash Gupta, Sharut Gupta, Liangqiong Qu, Alvin Ihsani, Bernardo C. Bizzo, Yuhong Wen, Varun Buch, Meesam Shah, Felipe Kitamura, Matheus Mendonça, Vitor Lavor, Ahmed Harouni, Colin Compas, Jesse Tetreault, Prerna Dogra, Yan Cheng, Selnur Erdal, Richard D. White, Behrooz Hashemian, Thomas J. Schultz, Miao Zhang, Adam McCarthy, B. Min Yun, Elshaimaa Sharaf, Katharina Viktoria Hoebel, Jay B. Patel, Bryan Chen, Sean Ko, Evan Leibovitz, Etta D. Pisano, Laura Coombs, Daguang Xu, Keith J. Dreyer, Ittai Dayan, Ram C. Naidu, Mona Flores, Daniel L. Rubin, Jayashree Kalpathy-Cramer - Lecture notes in computer science, DART/DCL@MICCAI 2020 cited by 124

  20. BioPortal: ontologies and integrated data resources at the click of a mouse

    Authors: Natalya Fridman Noy, Nigam H. Shah, Patricia L. Whetzel, Benjamin Dai, Michael Dorf, Nicholas Griffith, Clément Jonquet, Daniel L. Rubin, Margaret-Anne D. Storey, Christopher G. Chute, Mark A. Musen - Nucleic Acids Research, Nucleic Acids Res. 2009 cited by 884

  21. RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR

    Authors: Yuyin Zhou, Shih-Cheng Huang, Jason Alan Fries, Alaa Youssef, Timothy J. Amrhein, Marcello Chang, Imon Banerjee, Daniel L. Rubin, Lei Xing, Nigam Shah, Matthew P. Lungren - arXiv (Cornell University), CoRR 2021 cited by 47

  22. Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs

    Authors: Jared Dunnmon, Darvin Yi, Curtis P. Langlotz, Christopher Ré, Daniel L. Rubin, Matthew P. Lungren - Radiology 2018 cited by 209

  23. Progression of Photoreceptor Degeneration in Geographic Atrophy Secondary to Age-related Macular Degeneration

    Authors: Maximilian Pfau, Leon von der Emde, Luís de Sisternes, Joelle Hallak, Theodore Leng, Steffen Schmitz-Valckenberg, Frank G. Holz, Monika Fleckenstein, Daniel L. Rubin - JAMA Ophthalmology 2020 cited by 144

  24. Integrating Al Algorithms into the Clinical Workflow

    Authors: Krishna Juluru, Hao-Hsin Shih, Krishna Nand Keshava Murthy, Pierre Elnajjar, Amin El-Rowmeim, Christopher J. Roth, Brad Genereaux, Josef J. Fox, Eliot L. Siegel, Daniel L. Rubin - Radiology Artificial Intelligence 2021 cited by 74