Nathan Gaw

1.3k total citations
20 papers, 534 citations indexed

About

Nathan Gaw is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Cognitive Neuroscience. According to data from OpenAlex, Nathan Gaw has authored 20 papers receiving a total of 534 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Radiology, Nuclear Medicine and Imaging, 5 papers in Artificial Intelligence and 3 papers in Cognitive Neuroscience. Recurrent topics in Nathan Gaw's work include MRI in cancer diagnosis (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Migraine and Headache Studies (2 papers). Nathan Gaw is often cited by papers focused on MRI in cancer diagnosis (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Migraine and Headache Studies (2 papers). Nathan Gaw collaborates with scholars based in United States, United Kingdom and Greece. Nathan Gaw's co-authors include Teresa Wu, Jing Li, Todd J. Schwedt, Catherine D. Chong, Yinlin Fu, Mostafa Reisi Gahrooei, Safoora Yousefi, Ken Chang, Nishanth Arun and Praveer Singh and has published in prestigious journals such as Scientific Reports, Expert Systems with Applications and Allergy.

In The Last Decade

Nathan Gaw

15 papers receiving 526 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Nathan Gaw United States 8 176 172 131 95 72 20 534
April Khademi Canada 14 232 1.3× 61 0.4× 134 1.0× 60 0.6× 15 0.2× 48 525
Dimosthenis Tsagkrasoulis Greece 6 173 1.0× 104 0.6× 91 0.7× 262 2.8× 14 0.2× 11 631
Alberto F. Goldszal United States 8 156 0.9× 59 0.3× 60 0.5× 74 0.8× 31 0.4× 14 385
Gang Yu China 15 256 1.5× 81 0.5× 145 1.1× 115 1.2× 8 0.1× 48 718
Vasileios C. Pezoulas Greece 13 89 0.5× 134 0.8× 161 1.2× 432 4.5× 9 0.1× 71 968
Domenico Diacono Italy 14 169 1.0× 110 0.6× 186 1.4× 122 1.3× 5 0.1× 38 579
Claudio Stamile France 13 189 1.1× 40 0.2× 63 0.5× 88 0.9× 131 1.8× 27 488
Do‐Young Kang South Korea 16 159 0.9× 140 0.8× 75 0.6× 80 0.8× 14 0.2× 47 693
Jacob Levman Canada 12 229 1.3× 31 0.2× 167 1.3× 76 0.8× 12 0.2× 33 564
Ana I. L. Namburete United Kingdom 15 216 1.2× 36 0.2× 205 1.6× 92 1.0× 9 0.1× 41 717

Countries citing papers authored by Nathan Gaw

Since Specialization
Citations

This map shows the geographic impact of Nathan Gaw'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 Nathan Gaw with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nathan Gaw more than expected).

Fields of papers citing papers by Nathan Gaw

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nathan Gaw. 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 Nathan Gaw. The network helps show where Nathan Gaw may publish in the future.

Co-authorship network of co-authors of Nathan Gaw

This figure shows the co-authorship network connecting the top 25 collaborators of Nathan Gaw. A scholar is included among the top collaborators of Nathan Gaw 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 Nathan Gaw. Nathan Gaw is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Schleich, Florence, Stelios Loukides, Rekha Chaudhuri, et al.. (2025). Mepolizumab Effectiveness in Severe Asthma With/Without Chronic Rhinosinusitis With Nasal Polyps: Real‐World Pooled Analysis. Allergy. 80(9). 2557–2571.
2.
Lunday, Brian J., et al.. (2024). Investigating Regional Communication Network Robustness of an Asymmetric String-of-Pearls Satellite Constellation Design Framework. Journal of Spacecraft and Rockets. 61(3). 798–807.
3.
4.
Cox, Bruce, et al.. (2024). Garbage In ≠ Garbage Out: Exploring GAN Resilience to Image Training Set Degradations. Expert Systems with Applications. 250. 123902–123902. 1 indexed citations
5.
6.
Gaw, Nathan, et al.. (2023). Toward automated instructor pilots in legacy Air Force systems: Physiology-based flight difficulty classification via machine learning. Expert Systems with Applications. 231. 120711–120711. 7 indexed citations
7.
Gahrooei, Mostafa Reisi, et al.. (2022). Robust coupled tensor decomposition and feature extraction for multimodal medical data. 13(2). 117–131. 3 indexed citations
8.
9.
Gaw, Nathan, Jing Li, & Hyunsoo Yoon. (2022). A Novel Semi-Supervised Learning Model for Smartphone-Based Health Telemonitoring. IEEE Transactions on Automation Science and Engineering. 21(1). 428–441.
10.
Arun, Nishanth, Nathan Gaw, Praveer Singh, et al.. (2021). Assessing the Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging. Radiology Artificial Intelligence. 3(6). e200267–e200267. 147 indexed citations
11.
Gaw, Nathan, Safoora Yousefi, & Mostafa Reisi Gahrooei. (2021). Multimodal data fusion for systems improvement: A review. IISE Transactions. 54(11). 1098–1116. 50 indexed citations
12.
Chang, Ken, Andrew Beers, Jay Patel, et al.. (2020). Multi-Institutional Assessment and Crowdsourcing Evaluation of Deep Learning for Automated Classification of Breast Density. Journal of the American College of Radiology. 17(12). 1653–1662. 37 indexed citations
13.
Yoon, Hyunsoo & Nathan Gaw. (2020). A novel multi-task linear mixed model for smartphone-based telemonitoring. Expert Systems with Applications. 164. 113809–113809. 10 indexed citations
14.
Gaw, Nathan, Andrea Hawkins‐Daarud, Leland Hu, et al.. (2019). Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI. Scientific Reports. 9(1). 10063–10063. 62 indexed citations
15.
Swanson, Kristin R., Nathan Gaw, Andrea Hawkins‐Daarud, et al.. (2017). NIMG-74. RADIOMICS OF TUMOR INVASION 2.0: COMBINING MECHANISTIC TUMOR INVASION MODELS WITH MACHINE LEARNING MODELS TO ACCURATELY PREDICT TUMOR INVASION IN HUMAN GLIOBLASTOMA PATIENTS. Neuro-Oncology. 19(suppl_6). vi159–vi159. 1 indexed citations
16.
Gaw, Nathan, Todd J. Schwedt, Catherine D. Chong, Teresa Wu, & Jing Li. (2017). A clinical decision support system using multi-modality imaging data for disease diagnosis. 8(1). 36–46. 10 indexed citations
17.
Gaw, Nathan, et al.. (2016). Automatic Student Performance Analysis and Monitoring. International Journal of Innovative Research in Computer and Communication Engineering. 4(1). 33–38. 1 indexed citations
18.
Chong, Catherine D., Nathan Gaw, Yinlin Fu, et al.. (2016). Migraine classification using magnetic resonance imaging resting-state functional connectivity data. Cephalalgia. 37(9). 828–844. 99 indexed citations
19.
Schwedt, Todd J., Catherine D. Chong, Teresa Wu, et al.. (2015). Accurate Classification of Chronic Migraine via Brain Magnetic Resonance Imaging. Headache The Journal of Head and Face Pain. 55(6). 762–777. 101 indexed citations
20.
Edwards, Nichol M., Nathan Gaw, Audrey Harkness, et al.. (2013). RCPE UK Consensus Conference on ‘Acute Medicine: improving quality of care through effective patient flow – it’s everyone’s business!’. The Journal of the Royal College of Physicians of Edinburgh. 43(4). 316–317. 2 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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