Victor J. Catullo

697 total citations
8 papers, 503 citations indexed

About

Victor J. Catullo is a scholar working on Artificial Intelligence, Pulmonary and Respiratory Medicine and Oncology. According to data from OpenAlex, Victor J. Catullo has authored 8 papers receiving a total of 503 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 5 papers in Pulmonary and Respiratory Medicine and 4 papers in Oncology. Recurrent topics in Victor J. Catullo's work include AI in cancer detection (7 papers), Digital Radiography and Breast Imaging (5 papers) and Global Cancer Incidence and Screening (3 papers). Victor J. Catullo is often cited by papers focused on AI in cancer detection (7 papers), Digital Radiography and Breast Imaging (5 papers) and Global Cancer Incidence and Screening (3 papers). Victor J. Catullo collaborates with scholars based in United States. Victor J. Catullo's co-authors include David Gur, Marie A. Ganott, Jules H. Sumkin, Denise M. Chough, Andriy I. Bandos, Amy E. Kelly, Margarita L. Zuley, Amy Lu, Christiane M. Hakim and Gordon S. Abrams and has published in prestigious journals such as Radiology, American Journal of Roentgenology and Medical Physics.

In The Last Decade

Victor J. Catullo

8 papers receiving 490 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Victor J. Catullo United States 8 400 341 309 131 71 8 503
Lawrence J. Moss United States 5 340 0.8× 282 0.8× 186 0.6× 187 1.4× 47 0.7× 7 462
Ronald L. Perrin United States 7 540 1.4× 477 1.4× 448 1.4× 178 1.4× 108 1.5× 10 703
Chris Peressotti Canada 8 382 1.0× 246 0.7× 270 0.9× 141 1.1× 87 1.2× 10 489
Cathy S. Cohen United States 9 187 0.5× 241 0.7× 201 0.7× 174 1.3× 25 0.4× 13 390
Ruben E. van Engen Netherlands 14 771 1.9× 398 1.2× 579 1.9× 216 1.6× 213 3.0× 44 901
Ray C. Mayo United States 10 167 0.4× 146 0.4× 210 0.7× 63 0.5× 46 0.6× 16 422
Elisabetta Bezzon Italy 9 308 0.8× 191 0.6× 234 0.8× 97 0.7× 85 1.2× 16 421
Vivianne Freitas Canada 10 148 0.4× 151 0.4× 212 0.7× 116 0.9× 31 0.4× 35 385
Madhavi Raghu United States 8 539 1.3× 511 1.5× 375 1.2× 221 1.7× 85 1.2× 13 740
Paula Willsher United Kingdom 8 349 0.9× 270 0.8× 230 0.7× 129 1.0× 76 1.1× 9 437

Countries citing papers authored by Victor J. Catullo

Since Specialization
Citations

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

Fields of papers citing papers by Victor J. Catullo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Victor J. Catullo. 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 Victor J. Catullo. The network helps show where Victor J. Catullo may publish in the future.

Co-authorship network of co-authors of Victor J. Catullo

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

All Works

8 of 8 papers shown
1.
Sumkin, Jules H., Marie A. Ganott, Denise M. Chough, et al.. (2015). Recall Rate Reduction with Tomosynthesis During Baseline Screening Examinations. Academic Radiology. 22(12). 1477–1482. 17 indexed citations
2.
Hakim, Christiane M., Victor J. Catullo, Denise M. Chough, et al.. (2015). Effect of the Availability of Prior Full-Field Digital Mammography and Digital Breast Tomosynthesis Images on the Interpretation of Mammograms. Radiology. 276(1). 65–72. 22 indexed citations
3.
Zuley, Margarita L., Victor J. Catullo, Denise M. Chough, et al.. (2014). Comparison of Two-dimensional Synthesized Mammograms versus Original Digital Mammograms Alone and in Combination with Tomosynthesis Images. Radiology. 271(3). 664–671. 144 indexed citations
4.
Zuley, Margarita L., Andriy I. Bandos, Marie A. Ganott, et al.. (2012). Digital Breast Tomosynthesis versus Supplemental Diagnostic Mammographic Views for Evaluation of Noncalcified Breast Lesions. Radiology. 266(1). 89–95. 128 indexed citations
5.
Zheng, Bin, Margarita L. Zuley, Jules H. Sumkin, et al.. (2008). Detection of breast abnormalities using a prototype resonance electrical impedance spectroscopy system: A preliminary study. Medical Physics. 35(7Part1). 3041–3048. 11 indexed citations
6.
Good, Walter F., Gordon S. Abrams, Victor J. Catullo, et al.. (2008). Digital Breast Tomosynthesis: A Pilot Observer Study. American Journal of Roentgenology. 190(4). 865–869. 115 indexed citations
7.
Ganott, Marie A., Jules H. Sumkin, Jill L. King, et al.. (2006). Screening Mammography: Do Women Prefer a Higher Recall Rate Given the Possibility of Earlier Detection of Cancer?. Radiology. 238(3). 793–800. 42 indexed citations
8.
Zheng, Bin, Joseph K. Leader, Gordon S. Abrams, et al.. (2004). Computer-Aided Detection Schemes: The Effect of Limiting the Number of Cued Regions in Each Case. American Journal of Roentgenology. 182(3). 579–583. 24 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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