Nathan Ing

805 total citations
11 papers, 411 citations indexed

About

Nathan Ing is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Nathan Ing has authored 11 papers receiving a total of 411 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 6 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Nathan Ing's work include Radiomics and Machine Learning in Medical Imaging (6 papers), AI in cancer detection (6 papers) and Single-cell and spatial transcriptomics (2 papers). Nathan Ing is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (6 papers), AI in cancer detection (6 papers) and Single-cell and spatial transcriptomics (2 papers). Nathan Ing collaborates with scholars based in United States, Canada and Australia. Nathan Ing's co-authors include Arkadiusz Gertych, Beatrice S. Knudsen, Zhaoxuan Ma, Szczepan Cierniak, Samuel Guzman, Żaneta Świderska-Chadaj, Tomasz Markiewicz, Ann E. Walts, Mahul B. Amin and Kenneth Gouin and has published in prestigious journals such as Nature Communications, Cancer Research and Scientific Reports.

In The Last Decade

Nathan Ing

10 papers receiving 400 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Nathan Ing United States	6	217	165	107	103	80	11	411
Vipul Baxi United States	6	206 0.9×	205 1.2×	47 0.4×	63 0.6×	112 1.4×	16	446
Behnaz Abdollahi United States	8	158 0.7×	207 1.3×	96 0.9×	101 1.0×	143 1.8×	12	482
John Maddison United Kingdom	8	215 1.0×	295 1.8×	66 0.6×	52 0.5×	189 2.4×	18	542
Qitao Huang China	11	226 1.0×	201 1.2×	172 1.6×	112 1.1×	167 2.1×	25	613
Michel E. Vandenberghe United Kingdom	9	135 0.6×	169 1.0×	90 0.8×	51 0.5×	117 1.5×	19	393
Hubert G. Bartels United States	13	128 0.6×	78 0.5×	99 0.9×	98 1.0×	105 1.3×	52	459
Kate Lillard Japan	6	161 0.7×	136 0.8×	71 0.7×	40 0.4×	145 1.8×	7	390
Günter Schmidt United Kingdom	13	110 0.5×	144 0.9×	53 0.5×	53 0.5×	133 1.7×	28	420
Maha Shady United States	5	354 1.6×	270 1.6×	97 0.9×	36 0.3×	70 0.9×	10	522
Peter Truszkowski United States	2	211 1.0×	187 1.1×	45 0.4×	50 0.5×	60 0.8×	2	341

Countries citing papers authored by Nathan Ing

Since Specialization

Citations

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

Fields of papers citing papers by Nathan Ing

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nathan Ing

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

All Works

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11 of 11 papers shown

Mebane, Richard H., Teia Noel, Nathan Ing, et al.. (2025). Spatial transcriptomic analysis of immune checkpoint blockade response in triple negative breast cancers with tertiary lymphoid structures. iScience. 28(7). 112808–112808. 3 indexed citations

Lawson, Michael, Yuji Ishitsuka, Zhenmin Hong, et al.. (2024). 208 High resolution in-situ multiomic analysis of FFPE tissue at scale on the G4X spatial sequencer. Regular and Young Investigator Award Abstracts. A237–A237. 1 indexed citations

Karimzadeh, Mehran, Nathan Ing, Yoona Yang, et al.. (2023). The molecular consequences of androgen activity in the human breast. Cell Genomics. 3(3). 100272–100272. 15 indexed citations

Gouin, Kenneth, Nathan Ing, Jasmine Plummer, et al.. (2021). An N-Cadherin 2 expressing epithelial cell subpopulation predicts response to surgery, chemotherapy and immunotherapy in bladder cancer. Nature Communications. 12(1). 4906–4906. 103 indexed citations

Ing, Nathan, et al.. (2021). Trends and Determinants of Self-reported Aspirin Use Among Patients With Diabetes Stratified by Presence and Risk of Cardiovascular Diseases: A Repeated Pan-Canadian Cross-sectional Study. Canadian Journal of Diabetes. 46(4). 361–368.e5.

Gertych, Arkadiusz, Żaneta Świderska-Chadaj, Zhaoxuan Ma, et al.. (2019). Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides. Scientific Reports. 9(1). 1483–1483. 119 indexed citations

Ing, Nathan, Jakub M. Tomczak, Isla P. Garraway, et al.. (2018). A deep multiple instance model to predict prostate cancer metastasis from nuclear morphology. 2 indexed citations

Huang, Fangjin, Nathan Ing, Eric L. Miller, et al.. (2018). Abstract B094: Quantitative digital image analysis and machine learning for staging of prostate cancer at diagnosis. Cancer Research. 78(16_Supplement). B094–B094. 5 indexed citations

Ma, Zhaoxuan, Jiayun Li, Corey Arnold, et al.. (2018). Semantic segmentation for prostate cancer grading by convolutional neural networks. 46–46. 49 indexed citations

10.

Ing, Nathan, Fangjin Huang, Andrew B. Conley, et al.. (2017). A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcome. Scientific Reports. 7(1). 13190–13190. 29 indexed citations

11.

Gertych, Arkadiusz, Nathan Ing, Zhaoxuan Ma, et al.. (2015). Machine learning approaches to analyze histological images of tissues from radical prostatectomies. Computerized Medical Imaging and Graphics. 46. 197–208. 85 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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