Daniel L. Rubin

25.8k total citations · 6 hit papers
366 papers, 15.9k citations indexed

About

Daniel L. Rubin is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Daniel L. Rubin has authored 366 papers receiving a total of 15.9k indexed citations (citations by other indexed papers that have themselves been cited), including 199 papers in Radiology, Nuclear Medicine and Imaging, 148 papers in Artificial Intelligence and 109 papers in Molecular Biology. Recurrent topics in Daniel L. Rubin's work include Radiomics and Machine Learning in Medical Imaging (111 papers), Biomedical Text Mining and Ontologies (91 papers) and AI in cancer detection (78 papers). Daniel L. Rubin is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (111 papers), Biomedical Text Mining and Ontologies (91 papers) and AI in cancer detection (78 papers). Daniel L. Rubin collaborates with scholars based in United States, China and Canada. Daniel L. Rubin's co-authors include Assaf Hoogi, Sandy Napel, Luís de Sisternes, Alfiia Galimzianova, Gerald J. Berry, Russ B. Altman, Mark A. Musen, Francisco Javier Giménez Fuentes‐Guerra, Bradley J. Erickson and Christopher Ré and has published in prestigious journals such as New England Journal of Medicine, Nucleic Acids Research and Nature Communications.

In The Last Decade

Daniel L. Rubin

356 papers receiving 15.5k citations

Hit Papers

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What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

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

2017 722 citations Alfiia Galimzianova, Assaf Hoogi et al. profile →
Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

2016 680 citations Daniel L. Rubin et al. Nature Communications profile →
BioPortal: ontologies and integrated data resources at the click of a mouse

2009 556 citations Daniel L. Rubin, Margaret‐Anne Storey et al. profile →
Preparing Medical Imaging Data for Machine Learning

2020 553 citations Daniel L. Rubin et al. profile →
A curated mammography data set for use in computer-aided detection and diagnosis research

2017 493 citations Francisco Javier Giménez Fuentes‐Guerra, Assaf Hoogi et al. profile →
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging

2023 89 citations Liangqiong Qu, Daniel L. Rubin et al. IEEE Transactions on Medical Imaging profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Daniel L. Rubin	8.2k	5.9k	2.9k	2.5k	2.2k	366	15.9k
Jeroen van der Laak	7.2k 0.9×	7.8k 1.3×	1.6k 0.5×	1.9k 0.8×	4.1k 1.9×	207	17.0k
Geert Litjens	8.4k 1.0×	7.9k 1.3×	735 0.2×	3.1k 1.3×	4.4k 2.0×	101	16.1k
Clara I. Sá‎nchez	8.3k 1.0×	5.6k 0.9×	513 0.2×	1.8k 0.7×	3.9k 1.8×	108	14.3k
Anant Madabhushi	12.0k 1.4×	11.2k 1.9×	2.9k 1.0×	4.1k 1.6×	6.1k 2.8×	544	23.3k
Francesco Ciompi	6.6k 0.8×	5.7k 1.0×	497 0.2×	2.1k 0.8×	3.4k 1.6×	103	12.6k
Andre Esteva	3.6k 0.4×	4.9k 0.8×	878 0.3×	902 0.4×	1.5k 0.7×	30	11.9k
Ronald M. Summers	9.3k 1.1×	5.2k 0.9×	602 0.2×	3.5k 1.4×	4.3k 2.0×	501	18.9k
Jianhua Yao	4.5k 0.6×	3.3k 0.6×	1.3k 0.4×	1.5k 0.6×	2.6k 1.2×	377	12.7k
Hao Chen	5.9k 0.7×	6.8k 1.1×	684 0.2×	1.2k 0.5×	9.7k 4.5×	354	19.4k
Tianfu Wang	3.3k 0.4×	3.3k 0.6×	1.1k 0.4×	728 0.3×	2.8k 1.3×	409	12.3k

Countries citing papers authored by Daniel L. Rubin

Since Specialization

Citations

This map shows the geographic impact of Daniel L. Rubin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel L. Rubin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel L. Rubin more than expected).

Fields of papers citing papers by Daniel L. Rubin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel L. Rubin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel L. Rubin. The network helps show where Daniel L. Rubin may publish in the future.

Co-authorship network of co-authors of Daniel L. Rubin

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel L. Rubin. A scholar is included among the top collaborators of Daniel L. Rubin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daniel L. Rubin. Daniel L. Rubin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Kurian, Allison W., et al.. (2024). Automated Extraction of Patient-Centered Outcomes After Breast Cancer Treatment: An Open-Source Large Language Model–Based Toolkit. JCO Clinical Cancer Informatics. 8(8). e2300258–e2300258. 6 indexed citations

Eminağa, Okyaz, Mahmoud Abbas, Jeanne Shen, et al.. (2023). PlexusNet: A neural network architectural concept for medical image classification. Computers in Biology and Medicine. 154. 106594–106594. 15 indexed citations

Qu, Liangqiong, Natasha Sheybani, K. Elizabeth Hawk, et al.. (2023). AI Transformers for Radiation Dose Reduction in Serial Whole-Body PET Scans. Radiology Artificial Intelligence. 5(3). e220246–e220246. 9 indexed citations

Razeghi, Orod, Mahmood Alhusseini, Muhammad Fazal, et al.. (2023). Atrial fibrillation ablation outcome prediction with a machine learning fusion framework incorporating cardiac computed tomography. Journal of Cardiovascular Electrophysiology. 34(5). 1164–1174. 17 indexed citations

Tang, Siyi, Orod Razeghi, Mahmood Alhusseini, et al.. (2022). Machine Learning–Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes. Circulation Arrhythmia and Electrophysiology. 15(8). e010850–e010850. 45 indexed citations

Yamashita, Rikiya, Minhaj Nur Alam, Alfiia Galimzianova, et al.. (2022). Toward Reduction in False-Positive Thyroid Nodule Biopsies with a Deep Learning–based Risk Stratification System Using US Cine-Clip Images. Radiology Artificial Intelligence. 4(3). e210174–e210174. 11 indexed citations

Batlle, Juan, Keith J. Dreyer, Bibb Allen, et al.. (2021). Data Sharing of Imaging in an Evolving Health Care World: Report of the ACR Data Sharing Workgroup, Part 1: Data Ethics of Privacy, Consent, and Anonymization. Journal of the American College of Radiology. 18(12). 1646–1654. 18 indexed citations

Yamashita, Rikiya, et al.. (2021). Learning Domain-Agnostic Visual Representation for Computational Pathology Using Medically-Irrelevant Style Transfer Augmentation. IEEE Transactions on Medical Imaging. 40(12). 3945–3954. 40 indexed citations

Baratto, Lucia, K. Elizabeth Hawk, Ashok J. Theruvath, et al.. (2021). Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure. European Journal of Nuclear Medicine and Molecular Imaging. 48(9). 2771–2781. 58 indexed citations

10.

Emde, Leon von der, Maximilian Pfau, Frank G. Holz, et al.. (2021). AI-based structure-function correlation in age-related macular degeneration. Eye. 35(8). 2110–2118. 15 indexed citations

11.

Sieh, Weiva, Joseph H. Rothstein, Robert J. Klein, et al.. (2020). Identification of 31 loci for mammographic density phenotypes and their associations with breast cancer risk. Nature Communications. 11(1). 5116–5116. 23 indexed citations

12.

Fuentes‐Guerra, Francisco Javier Giménez, et al.. (2019). A Probabilistic Model to Support Radiologists’ Classification Decisions in Mammography Practice. Medical Decision Making. 39(3). 208–216. 4 indexed citations

13.

Hatamizadeh, Ali, et al.. (2019). DALS: Deep Active Lesion Segmentation. arXiv (Cornell University).

14.

Zaharchuk, Greg, Enhao Gong, Max Wintermark, Daniel L. Rubin, & Curtis P. Langlotz. (2018). Deep Learning in Neuroradiology. American Journal of Neuroradiology. 39(10). 1776–1784. 215 indexed citations

15.

Alexeeff, Stacey, Jafi A. Lipson, Ninah Achacoso, et al.. (2017). Age at Menarche and Late Adolescent Adiposity Associated with Mammographic Density on Processed Digital Mammograms in 24,840 Women. Cancer Epidemiology Biomarkers & Prevention. 26(9). 1450–1458. 18 indexed citations

16.

Sisternes, Luís de, Gowtham Jonna, Margaret A. Greven, et al.. (2017). Individual Drusen Segmentation and Repeatability and Reproducibility of Their Automated Quantification in Optical Coherence Tomography Images. Translational Vision Science & Technology. 6(1). 12–12. 20 indexed citations

17.

Achrol, Achal S., Lex A. Mitchell, Joshua Loya, et al.. (2016). Computational Identification of Tumor Anatomic Location Associated with Survival in 2 Large Cohorts of Human Primary Glioblastomas. American Journal of Neuroradiology. 37(4). 621–628. 27 indexed citations

18.

Nair, Viswam S., Olivier Gevaert, Guido Davidzon, et al.. (2012). Prognostic PET 18F-FDG Uptake Imaging Features Are Associated with Major Oncogenomic Alterations in Patients with Resected Non–Small Cell Lung Cancer. Cancer Research. 72(15). 3725–3734. 103 indexed citations

19.

Mejino, José L. V., et al.. (2008). Web service access to semantic web ontologies for data annotation.. PubMed. 946–946. 2 indexed citations

20.

Rubin, Daniel L., Suzanna Lewis, Chris Mungall, et al.. (2006). The National Center for Biomedical Ontology: Advancing Biomedicine through Structured \nOrganization of Scientific Knowledge. eScholarship (California Digital Library). 110 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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