Martin C. Stumpe

15.7k total citations · 4 hit papers
50 papers, 7.6k citations indexed

About

Martin C. Stumpe is a scholar working on Artificial Intelligence, Molecular Biology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Martin C. Stumpe has authored 50 papers receiving a total of 7.6k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 14 papers in Molecular Biology and 10 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Martin C. Stumpe's work include AI in cancer detection (13 papers), Cancer Genomics and Diagnostics (9 papers) and Radiomics and Machine Learning in Medical Imaging (9 papers). Martin C. Stumpe is often cited by papers focused on AI in cancer detection (13 papers), Cancer Genomics and Diagnostics (9 papers) and Radiomics and Machine Learning in Medical Imaging (9 papers). Martin C. Stumpe collaborates with scholars based in United States, Germany and United Kingdom. Martin C. Stumpe's co-authors include Lily Peng, Helmut Grubmüller, Marc Coram, Jessica L. Mega, Kim Ramasamy, Arunachalam Narayanaswamy, Philip Nelson, Dale R. Webster, Varun Gulshan and Kasumi Widner and has published in prestigious journals such as Journal of the American Chemical Society, JAMA and Nature Medicine.

In The Last Decade

Martin C. Stumpe

46 papers receiving 7.3k citations

Hit Papers

align trajectories

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.

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

2016 4.5k citations Varun Gulshan, Lily Peng et al. JAMA profile →
KeplerPresearch Data Conditioning II - A Bayesian Approach to Systematic Error Correction

2012 349 citations Jeffrey C. Smith, Martin C. Stumpe et al. profile →
Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer

2018 308 citations Robert MacDonald, Yun Liu et al. profile →
Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

2019 302 citations Kunal Nagpal, Davis Foote et al. npj Digital Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Martin C. Stumpe United States	21	3.5k	2.1k	1.5k	1.0k	920	50	7.6k
Philip Nelson United States	47	2.8k 0.8×	1.3k 0.6×	1.5k 1.0×	826 0.8×	641 0.7×	123	10.2k
Andre Esteva United States	13	3.6k 1.0×	4.9k 2.3×	260 0.2×	1.5k 1.5×	2.0k 2.1×	30	11.9k
Susan M. Swetter United States	42	2.7k 0.8×	3.7k 1.8×	261 0.2×	1.0k 1.0×	1.2k 1.3×	141	15.1k
Justin Ko United States	22	2.6k 0.7×	3.7k 1.7×	209 0.1×	1.0k 1.0×	1.3k 1.4×	89	9.8k
Roberto A. Novoa United States	15	2.5k 0.7×	3.5k 1.7×	202 0.1×	1.0k 1.0×	1.2k 1.3×	78	8.7k
Aaron Lee United States	43	3.3k 0.9×	596 0.3×	3.3k 2.3×	354 0.4×	293 0.3×	249	7.4k
Marc Coram United States	19	2.9k 0.8×	1.2k 0.6×	1.6k 1.1×	740 0.7×	648 0.7×	30	6.2k
Jiang Liu China	53	5.7k 1.6×	955 0.4×	4.1k 2.8×	3.3k 3.2×	55 0.1×	728	12.7k
Kenji Suzuki Japan	58	3.5k 1.0×	2.0k 0.9×	100 0.1×	2.0k 2.0×	109 0.1×	529	13.4k
Guang Yang United Kingdom	55	4.7k 1.3×	1.7k 0.8×	83 0.1×	2.5k 2.5×	341 0.4×	449	11.3k

Countries citing papers authored by Martin C. Stumpe

Since Specialization

Citations

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

Fields of papers citing papers by Martin C. Stumpe

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Martin C. Stumpe. 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 Martin C. Stumpe. The network helps show where Martin C. Stumpe may publish in the future.

Co-authorship network of co-authors of Martin C. Stumpe

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Mueller, Erik T., et al.. (2025). A self-supervised framework for laboratory data imputation in electronic health records. Communications Medicine. 5(1). 251–251. 1 indexed citations

Nagpal, Kunal, et al.. (2024). Use of a deep learning model on H&E slides to predict nucleic acid yield for NGS testing.. Journal of Clinical Oncology. 42(16_suppl). e13589–e13589.

Hong, Sun Hae, Qiyuan Hu, Renyu Zhang, et al.. (2024). Efficient and Generalizable Prediction of Molecular Alterations in Multiple-Cancer Cohorts Using Hematoxylin and Eosin Whole Slide Images. Modern Pathology. 38(4). 100691–100691. 1 indexed citations

Stein, Michelle M., Paul Fields, Luca Lonini, et al.. (2023). 163 A multi-modal, pan-cancer atlas of tumor-immune states across primary and metastatic disease using a large, real-world database. SHILAP Revista de lepidopterología. A182–A184.

Michuda, Jackson, Alessandra Breschi, Catherine Igartua, et al.. (2023). Validation of a Transcriptome-Based Assay for Classifying Cancers of Unknown Primary Origin. Molecular Diagnosis & Therapy. 27(4). 499–511. 18 indexed citations

Rosasco, Mario G., Chi-Sing Ho, Tianyou Luo, et al.. (2023). Abstract 4692: Comparison of interassay similarity and cellular deconvolution in spatial transcriptomics data using Visum CytAssist. Cancer Research. 83(7_Supplement). 4692–4692. 1 indexed citations

BenTaieb, Aïcha, Ryan Jones, Rohan P. Joshi, et al.. (2022). Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides. Modern Pathology. 35(12). 1791–1803. 7 indexed citations

Epstein, Caroline G., Alexandria M. Bobe, Joshua S. K. Bell, et al.. (2020). Real-world Evidence of Diagnostic Testing and Treatment Patterns in US Patients With Breast Cancer With Implications for Treatment Biomarkers From RNA Sequencing Data. Clinical Breast Cancer. 21(4). e340–e361. 10 indexed citations

Nagpal, Kunal, Davis Foote, Yun Liu, et al.. (2019). Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. npj Digital Medicine. 2(1). 48–48. 302 indexed citations breakdown →

10.

Liu, Yun, Timo Kohlberger, Mohammad Norouzi, et al.. (2018). Artificial Intelligence–Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists. Archives of Pathology & Laboratory Medicine. 143(7). 859–868. 246 indexed citations

11.

Hipp, Jason, Donald J. Johann, Yun Chen, et al.. (2018). Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools. Journal of Pathology Informatics. 9(1). 45–45. 9 indexed citations

12.

Gulshan, Varun, Lily Peng, Marc Coram, et al.. (2016). Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 316(22). 2402–2402. 4490 indexed citations breakdown →

13.

Doran, Carolina, et al.. (2016). Exploration adjustment by ant colonies. Royal Society Open Science. 3(1). 150533–150533. 1 indexed citations

14.

Movshovitz-Attias, Yair, et al.. (2015). Ontological supervision for fine grained classification of Street View storefronts. 1693–1702. 40 indexed citations

15.

Jolles, Jolle W., et al.. (2014). The role of previous social experience on risk-taking and leadership in three-spined sticklebacks. Behavioral Ecology. 25(6). 1395–1401. 33 indexed citations

16.

Nakayama, Shinnosuke, Martin C. Stumpe, Andrea Manica, & Rufus A. Johnstone. (2013). Experience overrides personality differences in the tendency to follow but not in the tendency to lead. Proceedings of the Royal Society B Biological Sciences. 280(1769). 20131724–20131724. 29 indexed citations

17.

Pinter‐Wollman, Noa, et al.. (2013). Harvester ants use interactions to regulate forager activation and availability. Animal Behaviour. 86(1). 197–207. 103 indexed citations

18.

Smith, Jeffrey C., Martin C. Stumpe, J. Van Cleve, et al.. (2012). Removing the Noise and Systematics while Preserving the Signal - An Empirical Bayesian Approach to Kepler Light Curve Systematic Error Correction. AAS. 220.

19.

Balona, L. A., M. Breger, G. Catanzaro, et al.. (2012). Unusual high-frequency oscillations in the Kepler Scuti star KIC 4840675★. Monthly Notices of the Royal Astronomical Society. 424(2). 1187–1196. 6 indexed citations

20.

Stumpe, Martin C., et al.. (2009). Molecular Determinants of Snurportin 1 Ligand Affinity and Structural Response upon Binding. Biophysical Journal. 97(2). 581–589. 6 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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