Jason Hipp

5.2k citations

58 papers · 2.9k indexed · 2 hit papers · h-index 24

Impact in

Health Informatics top 0.2%
- Artificial Intelligence in Healthcare and Education
Biophysics top 1%
- Cell Image Analysis Techniques

Papers in

Biophysics 9
- Cell Image Analysis Techniques 9
Artificial Intelligence 26
- AI in cancer detection 26

Co-authors: Martin C. Stumpe Yun Liu Anthony Atala Robert MacDonald Michael D. West Robert Lanza Irina Klimanskaya Kourous A. Rezai
Journals: Journal of Pathology Informatics (11 papers)Analytical Cellular Pathology (3 papers)Cancer Research (3 papers)British Journal of Urology (3 papers)Archives of Pathology & Laboratory Medicine (2 papers)
Partner nations: United States Canada United Kingdom

In The Last Decade

Jason Hipp

58 papers receiving 2.9k citations

Hit Papers

align trajectories log scale

Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer 2019 · 302 citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

2019 npj Digital Medicine
2018 The American Journal of Surgical Pathology

Peers

Countries citing papers authored by Jason Hipp

Since Specialization

Citations

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

Fields of papers citing papers by Jason Hipp

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Jason Hipp, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jason Hipp Line = papers co-authored together Jason Hipp links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

#	Work
1	Learning to predict RNA sequence expressions from whole slide images with applications for search and classification Communications Biology ·Victor K. Agyemang, Ye-Lin Jiao, Milad Sikaroudi, Jason Hipp, Hamid R. Tizhoosh	2023	24
2	Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer npj Digital Medicine ·Kunal Nagpal, Davis Foote, Yun Liu, Po-Hsuan Cameron Chen, Ellery Wulczyn, Fraser Elisabeth Tan, Niels Olson, Jenny L. Smith, Arash Mohtashamian, LI Guang-ta, Greg S. Corrado, Robert MacDonald, Lily H. Peng, Mahul B. Amin, Andrew Evans, Ankur R. Sangoi, Craig H. Mermel, Jason Hipp, Martin C. Stumpe Hit paper breakdown →	2019	302
3	Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools Journal of Pathology Informatics ·Jason Hipp, Donald J. Johann, Yun Chen, Anant Madabhushi, James Monaco, Jerome Cheng, Jaime Rodriguez‐Canales, Martin C. Stumpe, Gregory Riedlinger, Avi Z. Rosenberg, Jeffrey C. Hanson, Lakshmi P. Kunju, Michael R. Emmert‐Buck, Ulysses J. Balis, Michael A. Tangrea	2018	9
4	High-Throughput Microdissection for Next-Generation Sequencing PLoS ONE ·Avi Z. Rosenberg, Zhao Yan, Patricia Fetsch, Liqiang Xi, Mateja Kop, Mark Raffeld, Yun Chen, 聡森山, 西村まゆみ, Adele Blackler, Qiang Du, Jeffrey C. Hanson, Mark J. Roth, Armando Filie, Michael H. Roh, Michael R. Emmert‐Buck, Jason Hipp, Michael A. Tangrea	2016	17
5	eeDAP: An evaluation environment for digital and analog pathology Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE ·Brandon D. Gallas, Wei‐Chung Cheng, Marios A. Gavrielides, Tram Guilbeault, P. P., Adam Wunderlich, Jason Hipp, Stephen M. Hewitt	2014	1
6	Tryggo: Old norse for truth: The real truth about ground truth: New insights into the challenges of generating ground truth maps for WSI CAD algorithm evaluation Journal of Pathology Informatics ·Jason Hipp, Steven C. Smith, 橘内勇, David R. Lucas, Jennifer Hipp, Lakshmi P. Kunju, Ulysses J. Balis	2012	4
7	Image microarrays derived from tissue microarrays (IMA-TMA): New resource for computer-aided diagnostic algorithm development Journal of Pathology Informatics ·Jennifer Hipp, Jason Hipp, Michael J. Lim, Gaurav Sharma, Lauren B. Smith, Stephen M. Hewitt, Ulysses J. Balis	2012	1
8	Interleukin-1α Mediates the Antiproliferative Effects of 1,25-Dihydroxyvitamin D3 in Prostate Progenitor/Stem Cells Cancer Research ·Sophia L. Maund, Wendy W. Barclay, Laura D. Hover, Florence Vaysse, Guangchao Sui, Jason Hipp, James C. Fleet, Andrew Thorburn, Scott D. Cramer	2011	45
9	Image microarrays (IMA): Digital pathology’s missing tool Journal of Pathology Informatics ·Jason Hipp, Jerome Cheng, Liron Pantanowitz, Stephen M. Hewitt, Yukako Yagi, James Monaco, Anant Madabhushi, Jaime Rodriguez‐Canales, Jeffrey C. Hanson, Sinchita Roy‐Chowdhuri, Armando Filie, Michael D. Feldman, John E. Tomaszewski, Natalie Shih, Takeshi Takayama, Giuseppe Giaccone, Michael R. Emmert‐Buck, Ulysses J. Balis	2011	8
10	Why a pathology image should not be considered as a radiology image Journal of Pathology Informatics ·Jason Hipp, R Ebrahimi Jaberi, Carolyn C. Compton, Ulysses J. Balis	2011	21
11	Automated vector selection of SIVQ and parallel computing integration MATLABTM: Innovations supporting large-scale and high-throughput image analysis studies Journal of Pathology Informatics ·Jerome Cheng, Jason Hipp, James Monaco, David R. Lucas, Anant Madabhushi, Ulysses J. Balis	2011	9
12	SIVQ-aided laser capture microdissection: A tool for high-throughput expression profiling Journal of Pathology Informatics ·Jason Hipp, Jerome Cheng, Jeffrey C. Hanson, Wusheng Yan, Phil R. Taylor, Nan Hu, Jaime Rodriguez‐Canales, Jennifer Hipp, Michael A. Tangrea, Michael R. Emmert‐Buck, Ulysses J. Balis	2011	23
13	The need for the pathology community to sponsor a whole slide imaging repository with technical guidance from the pathology informatics community Journal of Pathology Informatics ·Jason Hipp, 橘内勇, Barbara J. McKenna, James Monaco, Anant Madabhushi, Jerome Cheng, Ulysses J. Balis	2011	15
14	Ethanol Alters the Osteogenic Differentiation of Amniotic Fluid‐Derived Stem Cells Alcoholism Clinical and Experimental Research ·Jennifer Hipp, Jason Hipp, Anthony Atala, Shay Söker	2010	8
15	Large-Scale Profiling of Archival Lymph Nodes Reveals Pervasive Remodeling of the Follicular Lymphoma Methylome Cancer Research ·Jonathan Keith Killian, Sven Bilke, Sean Davis, Robert L. Walker, M. Scott Killian, Erich Jaeger, Yidong Chen, Jason Hipp, Stefania Pittaluga, Mark Raffeld, Robert Cornelison, William I. Smith, Marina Bibikova, Jian-Bing Fan, Michael R. Emmert‐Buck, Elaine S. Jaffe, Paul S. Meltzer	2009	36
16	Microarray analysis of bladder smooth muscle from patients with myelomeningocele British Journal of Urology ·Jennifer Hipp, Jason Hipp, James J. Yoo, Anthony Atala, Karl‐Erik Andersson	2008	28
17	Glucocorticoid- and Protein Kinase A–Dependent Transcriptome Regulation in Airway Smooth Muscle American Journal of Respiratory Cell and Molecular Biology ·Barbara G. Ford, Deepak A. Deshpande, Matthew J. Loza, Rodolfo M. Pascual, Jason Hipp, Raymond B. Penn	2008	33
18	Using gene chips to identify organ‐specific, smooth muscle responses to experimental diabetes: potential applications to urological diseases British Journal of Urology ·Jason Hipp, Kelvin P. Davies, Moses Tar, Mira Valcic, William R. Kellogg, Arnold Melman, George J. Christ	2007	27
19	GeneChips in Stem Cell Research Methods in enzymology on CD-ROM/Methods in enzymology ·Jason Hipp, Anthony Atala	2006	4
20	Derivation and Comparative Assessment of Retinal Pigment Epithelium from Human Embryonic Stem Cells Using Transcriptomics Cloning and Stem Cells ·Irina Klimanskaya, Jason Hipp, Kourous A. Rezai, Michael D. West, Anthony Atala, Robert Lanza	2004	311

About Jason Hipp

Jason Hipp is a scholar working on Biophysics, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Urology and Cancer Research, having authored 58 papers that have together received 2.9k indexed citations. Recurring topics across this work include AI in cancer detection (26 papers), Radiomics and Machine Learning in Medical Imaging (12 papers), Cell Image Analysis Techniques (9 papers), Molecular Biology Techniques and Applications (7 papers), Cancer Genomics and Diagnostics (5 papers), Gene expression and cancer classification (5 papers), Biomedical Text Mining and Ontologies (5 papers) and Colorectal Cancer Screening and Detection (4 papers). The work is most often cited by research in Health Informatics (305 citations), Biophysics (263 citations), Artificial Intelligence (1.2k citations), Radiology, Nuclear Medicine and Imaging (849 citations) and Oncology (455 citations). Jason Hipp has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Martin C. Stumpe, Yun Liu, Anthony Atala, Robert MacDonald, Michael D. West, Robert Lanza, Irina Klimanskaya, Kourous A. Rezai, Greg S. Corrado and Timo Kohlberger. Their work appears in journals such as Journal of Pathology Informatics, Analytical Cellular Pathology, Cancer Research, British Journal of Urology and Archives of Pathology & Laboratory Medicine.

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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