J Šochman

1.1k citations
55 papers · 728 · h-index 12

Impact in

Papers in

J Šochman

49 papers receiving 678 citations

Peers

J Šochman
Comparison fields: 5 of 105
  • Computer Vision and Pattern Recognition 299
  • Cardiology and Cardiovascular Medicine 170
  • Nephrology 48
  • Automotive Engineering 70
  • Pathology and Forensic Medicine 84
Replace Rong Chen with:
Rong Chen China
Yuichiro Toda Japan
Hongtao Shi China
John J. Sampson United States
Hua Chai China
A Buda United States
Jörg Walter Germany
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Hung‐Ju Lin Taiwan
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Citations per field
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Citations per year

Countries citing papers authored by J Šochman

Since Specialization
Citations

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

Fields of papers citing papers by J Šochman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 55 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2005146
2 2012100
3 200281
4 199050
5 199646
6 200044
7 201140
8 201330
9 199219
10 200419
11 200513
12 200812
13 200911
14 201010
15 20109
16 20189
17
Insertion/deletion polymorphism in the angiotensin-converting enzyme gene in myocardial infarction survivors.
20019
18 20167
19
Prevention of contrast agent-induced renal impairment in patients with chronic renal insufficiency and heart disease by high-dose intravenous N-acetylcysteine: a pilot-ministudy.
20066
20
Feedback between Low-level and High-Level Image Processing
20076

About J Šochman

J Šochman is a scholar working on Cardiology and Cardiovascular Medicine, Surgery, Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine and Epidemiology, having authored 55 papers that have together received 728 indexed citations. Recurring topics across this work include Cardiac Valve Diseases and Treatments (11 papers), Cardiac Structural Anomalies and Repair (8 papers), Infective Endocarditis Diagnosis and Management (7 papers), Mechanical Circulatory Support Devices (6 papers), Transplantation: Methods and Outcomes (5 papers), Cardiac Arrhythmias and Treatments (4 papers), Cardiac Imaging and Diagnostics (4 papers) and Machine Learning and Algorithms (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (299 citations), Cardiology and Cardiovascular Medicine (170 citations), Nephrology (48 citations), Automotive Engineering (70 citations) and Pathology and Forensic Medicine (84 citations). J Šochman has collaborated with scholars based in Czechia, United States and Poland. Frequent co-authors include Jiřı́ Matas, J Peregrín, David Hogg, C. Caraffi, J Kolc, Dušan Pavčnik, Josef Rösch, Hans A. Timmermans, Luděk Červenka and Horst Bischof. Their work appears in journals such as Physiological Research, CardioVascular and Interventional Radiology, International Journal of Cardiology, Pacing and Clinical Electrophysiology and Bioscience Reports.

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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