Daniel Smutek

29 papers receiving 464 citations

Peers

Daniel Smutek
Comparison fields: 5 of 75
  • Health Informatics 12
  • Radiology, Nuclear Medicine and Imaging 164
  • Computer Vision and Pattern Recognition 168
  • Endocrinology, Diabetes and Metabolism 111
  • Artificial Intelligence 126
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Stavros Tsantis Greece
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Citations per year

Countries citing papers authored by Daniel Smutek

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Smutek

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 24 scholars most cited alongside Daniel Smutek, 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 Daniel Smutek Line = papers co-authored together Daniel Smutek links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2007103
2 200869
3 200351
4 201944
5 200736
6 201828
7
Autoimmune thyroid diseases in women with breast cancer and colorectal cancer.
200426
8 200823
9
3D extension of Haralick texture features for medical image analysis
200712
10 200112
11 20039
12
Quality measurement of lossy compression in medical imaging.
20059
13 20039
14
Representing clinical guidelines in UMl: a comparative study.
20028
15 20067
16 20055
17 20054
18
Quantitative indicators of sonographic image of thyroid gland and their relation to antithyroid antibodies in Hashimoto's lymphocytic thyroiditis.
20023
19
Relation between quantitative description of ultrasonographic image and clinical and laboratory findings in lymphocytic thyroiditis.
20033
20 20073

About Daniel Smutek

Daniel Smutek is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Endocrinology, Diabetes and Metabolism and Health Information Management, having authored 32 papers that have together received 482 indexed citations. Recurring topics across this work include AI in cancer detection (12 papers), Medical Image Segmentation Techniques (9 papers), Radiomics and Machine Learning in Medical Imaging (5 papers), Thyroid Disorders and Treatments (4 papers), Thyroid Cancer Diagnosis and Treatment (4 papers), Ultrasound Imaging and Elastography (3 papers), Artificial Intelligence in Healthcare (3 papers) and Image Retrieval and Classification Techniques (3 papers). The work is most often cited by research in Health Informatics (12 citations), Radiology, Nuclear Medicine and Imaging (164 citations), Computer Vision and Pattern Recognition (168 citations), Endocrinology, Diabetes and Metabolism (111 citations) and Artificial Intelligence (126 citations). Daniel Smutek has collaborated with scholars based in Czechia, Japan and Switzerland. Frequent co-authors include Akinobu Shimizu, Shigeru Nawano, Hidefumi Kobatake, Rena Ohno, Radim Šára, P Sucharda, Z Límanová, Jan Jiskra, Martin Švec and Tardi Tjahjadi. Their work appears in journals such as Computerized Medical Imaging and Graphics, Ultrasound in Medicine & Biology, Oncology Reports, Clinical & Experimental Immunology and Molecular Immunology.

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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