Mark D. Zarella

29 papers receiving 1.0k citations

Peers

Mark D. Zarella
Comparison fields: 5 of 106
  • Health Informatics 106
  • Biophysics 234
  • Artificial Intelligence 681
  • Radiology, Nuclear Medicine and Imaging 444
  • Computer Vision and Pattern Recognition 199
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Luke Geneslaw United States
Guillaume Jaume United States
Maschenka Balkenhol Netherlands
Vitor Werneck Krauss Silva United States
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Countries citing papers authored by Mark D. Zarella

Since Specialization
Citations

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

Fields of papers citing papers by Mark D. Zarella

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark D. Zarella. 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 Mark D. Zarella. The network helps show where Mark D. Zarella may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019268
2 2018248
3 2019242
4 202154
5 202147
6 200934
7 201730
8 202321
9 201520
10 202118
11 202215
12
Lymph Node Metastasis Status in Breast Carcinoma Can Be Predicted via Image Analysis of Tumor Histology.
201512
13 20199
14 20139
15 20177
16 20185
17 20185
18 20184
19
Pharmacological Dissection of Laminar Contributions to Intrinsic Optical Signals in the Retina
20063
20
The Origins of Stimulus Dependent Intrinsic Optical Signals of the Retina
20052

About Mark D. Zarella

Mark D. Zarella is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Biophysics, Computer Vision and Pattern Recognition and Molecular Biology, having authored 30 papers that have together received 1.1k indexed citations. Recurring topics across this work include AI in cancer detection (19 papers), Cell Image Analysis Techniques (8 papers), Radiomics and Machine Learning in Medical Imaging (7 papers), Visual perception and processing mechanisms (3 papers), Neural dynamics and brain function (3 papers), Breast Cancer Treatment Studies (2 papers), Artificial Intelligence in Healthcare and Education (2 papers) and HER2/EGFR in Cancer Research (2 papers). The work is most often cited by research in Health Informatics (106 citations), Biophysics (234 citations), Artificial Intelligence (681 citations), Radiology, Nuclear Medicine and Imaging (444 citations) and Computer Vision and Pattern Recognition (199 citations). Mark D. Zarella has collaborated with scholars based in United States, Netherlands and Sweden. Frequent co-authors include Marilyn M. Bui, Anil V. Parwani, Famke Aeffner, Douglas J. Hartman, Douglas Bowman, Navid Farahani, Esther Abels, Albert Xthona, Cleopatra Kozlowski and Liron Pantanowitz. Their work appears in journals such as Journal of Pathology Informatics, Archives of Pathology & Laboratory Medicine, The Journal of Pathology, Investigative Ophthalmology & Visual Science and Journal of Vision.

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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