James Monaco

1.0k citations

37 papers · 761 indexed · h-index 14

Impact in

Biophysics top 2%
- Cell Image Analysis Techniques
Computer Vision and Pattern Recognition top 5%
- Digital Imaging for Blood Diseases
- Medical Image Segmentation Techniques
- Image Retrieval and Classification Techniques

Papers in ⓘ

Artificial Intelligence 24
- AI in cancer detection 20
- Bayesian Methods and Mixture Models 5
Computer Vision and Pattern Recognition 21
- Medical Image Segmentation Techniques 11
- Image Retrieval and Classification Techniques 5
- Digital Imaging for Blood Diseases 5

Co-authors: Anant Madabhushi (26 shared papers)Michael D. Feldman (11 shared papers)Ajay Basavanhally (4 shared papers)Shridar Ganesan (3 shared papers)Gyan Bhanot (2 shared papers)J E Tomaszewski (1 shared paper)Shannon C. Agner (1 shared paper)Scott Doyle (5 shared papers)
Journals: Journal of Pathology Informatics (5 papers)Analytical Cellular Pathology (2 papers)Medical Image Analysis (2 papers)Clinical Chemistry and Laboratory Medicine (CCLM) (1 paper)IEEE Transactions on Image Processing (1 paper)
Partner nations: United States Netherlands Canada

In The Last Decade

James Monaco

35 papers receiving 747 citations

Peers

Countries citing papers authored by James Monaco

Since Specialization

Citations

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

Fields of papers citing papers by James Monaco

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside James Monaco, 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 James Monaco Line = papers co-authored together James Monaco links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

Showing the 20 most-cited of 37 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	Computerized Image-Based Detection and Grading of Lymphocytic Infiltration in HER2+ Breast Cancer Histopathology IEEE Transactions on Biomedical Engineering ·Ajay Basavanhally, Shridar Ganesan, Shannon C. Agner, James Monaco, Michael D. Feldman, J E Tomaszewski, Gyan Bhanot, Anant Madabhushi	2009	207
2	High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models Medical Image Analysis ·James Monaco, John E. Tomaszewski, Michael D. Feldman, Ian S. Hagemann, Mehdi Moradi, Parvin Mousavi, Alexander H. Boag, Chris Davidson, Purang Abolmaesumi, Anant Madabhushi	2010	90
3	Computer aided diagnostic tools aim to empower rather than replace pathologists: Lessons learned from computational chess Journal of Pathology Informatics ·Jason Hipp, Thomas J. Flotte, James Monaco, Jerome Cheng, Anant Madabhushi, Yukako Yagi, Jaime Rodriguez‐Canales, Michael R. Emmert‐Buck, Michael C. Dugan, Stephen M. Hewitt, Mehmet Toner, Ronald G. Tompkins, David R. Lucas, John R. Gilbertson, Ulysses J. Balis	2011	56
4	An active learning based classification strategy for the minority class problem: application to histopathology annotation BMC Bioinformatics ·Scott Doyle, James Monaco, Michaël Feldman, John Tomaszewski, Anant Madabhushi	2011	52
5	Towards Improved Cancer Diagnosis and Prognosis Using Analysis of Gene Expression Data and Computer Aided Imaging Experimental Biology and Medicine ·Gabriela Alexe, James Monaco, Scott Doyle, Ajay Basavanhally, Anupama Reddy, Michael Seiler, Shridar Ganesan, Gyan Bhanot, Anant Madabhushi	2009	32
6	Integrated diagnostics: a conceptual framework with examples Clinical Chemistry and Laboratory Medicine (CCLM) ·Anant Madabhushi, Scott Doyle, George Lee, Ajay Basavanhally, James Monaco, Steve Masters, John Tomaszewski, Michael D. Feldman	2010	29
7	Image Segmentation with Implicit Color Standardization Using Spatially Constrained Expectation Maximization: Detection of Nuclei Lecture notes in computer science ·James Monaco, J. Hipp, David R. Lucas, Stephen A. Smith, Ulysses J. Balis, Anant Madabhushi	2012	26
8	A knowledge representation framework for integration, classification of multi-scale imaging and non-imaging data: Preliminary results in predicting prostate cancer recurrence by fusing mass spectrometry and histology George Lee, Scott Doyle, James Monaco, Anant Madabhushi, Michael D. Feldman, Stephen R. Master, John E. Tomaszewski	2009	26
9	Class-specific weighting for Markov random field estimation: Application to medical image segmentation Medical Image Analysis ·James Monaco, Anant Madabhushi	2012	23
10	Markov Random Field driven Region-Based Active Contour Model (MaRACel): Application to Medical Image Segmentation Lecture notes in computer science ·Jun Xu, James Monaco, Anant Madabhushi	2010	19
11	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, Jeffrey Sica, Barbara J. McKenna, James Monaco, Anant Madabhushi, Jerome Cheng, Ulysses J. Balis	2011	15
12	Detection of prostate cancer on histopathology using color fractals and Probabilistic Pairwise Markov models PubMed ·Genong Yu, James Monaco, John Tomaszewski, Natalie Shih, Michael D. Feldman, Anant Madabhushi	2011	15
13	Transplant and mechanical circulatory support in patients with adult congenital heart disease Heart Failure Reviews ·James Monaco, Amber Khanna, Prateeti Khazanie	2020	14
14	Optimization of complex cancer morphology detection using the SIVQ pattern recognition algorithm. SHILAP Revista de lepidopterología ·Jason Hipp, Steven C. Smith, Jerome Cheng, Scott A. Tomlins, James Monaco, Anant Madabhushi, Lakshmi P. Kunju, Ulysses J. Balis	2012	13
15	Probabilistic pairwise Markov models: application to prostate cancer detection Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE ·James Monaco, John E. Tomaszewski, Michael D. Feldman, Mehdi Moradi, Parvin Mousavi, Alexander H. Boag, Chris Davidson, Purang Abolmaesumi, Anant Madabhushi	2009	12
16	Optimization of Complex Cancer Morphology Detection Using the SIVQ Pattern Recognition Algorithm Analytical Cellular Pathology ·Jason Hipp, Steven C. Smith, Jerome Cheng, Scott A. Tomlins, James Monaco, Anant Madabhushi, Lakshmi P. Kunju, Ulysses J. Balis	2012	12
17	Improving supervised classification accuracy using non-rigid multimodal image registration: detecting prostate cancer Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE ·Jonathan Chappelow, Satish E. Viswanath, James Monaco, Mark Rosen, John Tomaszewski, Michaël Feldman, Anant Madabhushi	2008	11
18	Integration of architectural and cytologic driven image algorithms for prostate adenocarcinoma identification. SHILAP Revista de lepidopterología ·Jason Hipp, James Monaco, Lakshmi P. Kunju, Jerome Cheng, Yukako Yagi, Jaime Rodriguez‐Canales, Michael R. Emmert‐Buck, Stephen M. Hewitt, Michael D. Feldman, John E. Tomaszewski, Mehmet Toner, Ronald G. Tompkins, Thomas J. Flotte, David R. Lucas, John R. Gilbertson, Anant Madabhushi, Ulysses J. Balis	2012	10
19	Evaluation of effects of JPEG2000 compression on a computer-aided detection system for prostate cancer on digitized histopathology Scott Doyle, James Monaco, Anant Madabhushi, Stefan Lindholm, Patric Ljung, Lance Ladic, John Tomaszewski, Michael D. Feldman	2010	10
20	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

About James Monaco

James Monaco is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Oncology and Radiology, Nuclear Medicine and Imaging, having authored 37 papers that have together received 761 indexed citations. Recurring topics across this work include AI in cancer detection (20 papers), Medical Image Segmentation Techniques (11 papers), Cell Image Analysis Techniques (5 papers), Image Retrieval and Classification Techniques (5 papers), Digital Imaging for Blood Diseases (5 papers), Colorectal Cancer Screening and Detection (5 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and Bayesian Methods and Mixture Models (5 papers). The work is most often cited by research in Biophysics (138 citations), Computer Vision and Pattern Recognition (345 citations), Artificial Intelligence (524 citations), Health Informatics (18 citations) and Radiology, Nuclear Medicine and Imaging (244 citations). James Monaco has collaborated with scholars based in United States, Netherlands and Canada. Frequent co-authors include Anant Madabhushi, Michael D. Feldman, Ajay Basavanhally, Shridar Ganesan, Gyan Bhanot, J E Tomaszewski, Shannon C. Agner, Scott Doyle, John Tomaszewski and Ulysses J. Balis. Their work appears in journals such as Journal of Pathology Informatics, Analytical Cellular Pathology, Medical Image Analysis, Clinical Chemistry and Laboratory Medicine (CCLM) and IEEE Transactions on Image Processing.

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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