Mark Eramian

1.2k citations
40 papers · 877 · 1 hit paper · h-index 12

Impact in

Papers in

Mark Eramian

39 papers receiving 831 citations

Mark Eramian's Hit Papers

Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network 2017 · 294 citations
2940+3+6Years since publication50100150200250

Peers

Mark Eramian
Comparison fields: 5 of 125
  • Health Informatics 50
  • Computer Vision and Pattern Recognition 282
  • Media Technology 107
  • Radiology, Nuclear Medicine and Imaging 237
  • Endocrinology, Diabetes and Metabolism 127
Replace Lin Han with:
Lin Han China
Jianning Chi China
Mohammad Hesam Hesamian Australia
Yongzhao Du China
Debdoot Sheet India
Bryan M. Williams United Kingdom
Weidong Sun China
Jinming Duan United Kingdom
Maxim Berman Belgium
Reza Azad Germany
Mark Eramian relative to Lin Han China Lin Han's profile →
Citations per field
00.5×10×15×20×24×
Lin Han · 1×
Citations per year

Countries citing papers authored by Mark Eramian

Since Specialization
Citations

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

Fields of papers citing papers by Mark Eramian

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 40 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network
Hit paper breakdown →
2017294
2 1997116
3 2016111
4 200540
5 200733
6 199927
7 201826
8 200922
9 201518
10 201917
11 201116
12 201515
13 202311
14 199711
15 201710
16 201310
17 202210
18 201710
19 20228
20 20198

About Mark Eramian

Mark Eramian is a scholar working on Computer Vision and Pattern Recognition, Plant Science, Molecular Biology, Radiology, Nuclear Medicine and Imaging and Ecology, having authored 40 papers that have together received 877 indexed citations. Recurring topics across this work include Smart Agriculture and AI (11 papers), Advanced Image and Video Retrieval Techniques (8 papers), Image and Signal Denoising Methods (5 papers), Remote Sensing in Agriculture (4 papers), Genetic and phenotypic traits in livestock (3 papers), Advanced Neural Network Applications (3 papers), Remote Sensing and LiDAR Applications (3 papers) and Image Enhancement Techniques (3 papers). The work is most often cited by research in Health Informatics (50 citations), Computer Vision and Pattern Recognition (282 citations), Media Technology (107 citations), Radiology, Nuclear Medicine and Imaging (237 citations) and Endocrinology, Diabetes and Metabolism (127 citations). Mark Eramian has collaborated with scholars based in Canada, United Kingdom and United States. Frequent co-authors include Jianning Chi, Yi Xin, Ekta Walia, Jimmy Wang, Gary Groot, Paul Babyn, R. G. Stockwell, L. Mansinha, Robert A. Schincariol and David Mould. Their work appears in journals such as Plant Phenomics, IEEE Transactions on Image Processing, Scientific Reports, Journal of Digital Imaging and The Visual Computer.

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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