Tomasz Markiewicz

1.8k total citations
70 papers, 1.2k citations indexed

About

Tomasz Markiewicz is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Tomasz Markiewicz has authored 70 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Artificial Intelligence, 27 papers in Computer Vision and Pattern Recognition and 15 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Tomasz Markiewicz's work include AI in cancer detection (27 papers), Radiomics and Machine Learning in Medical Imaging (11 papers) and Digital Imaging for Blood Diseases (10 papers). Tomasz Markiewicz is often cited by papers focused on AI in cancer detection (27 papers), Radiomics and Machine Learning in Medical Imaging (11 papers) and Digital Imaging for Blood Diseases (10 papers). Tomasz Markiewicz collaborates with scholars based in Poland, United States and Spain. Tomasz Markiewicz's co-authors include S. Osowski, Trần Hoài Linh, Krzysztof Siwek, Żaneta Świderska-Chadaj, Wojciech Kozłowski, Bartłomiej Grala, K. Brudzewski, Szczepan Cierniak, J Słodkowska and Arkadiusz Gertych and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and International Journal of Molecular Sciences.

In The Last Decade

Tomasz Markiewicz

65 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tomasz Markiewicz Poland 16 427 394 315 299 243 70 1.2k
M. Muthu Rama Krishnan India 20 463 1.1× 202 0.5× 138 0.4× 309 1.0× 71 0.3× 26 1.1k
Ali Mohammad Alqudah Jordan 21 418 1.0× 227 0.6× 126 0.4× 242 0.8× 129 0.5× 64 1.2k
José Rouco Spain 18 651 1.5× 214 0.5× 160 0.5× 524 1.8× 148 0.6× 49 1.5k
Shivajirao M. Jadhav India 15 474 1.1× 242 0.6× 145 0.5× 240 0.8× 173 0.7× 25 1.2k
Marcos Ortega Spain 24 278 0.7× 221 0.6× 179 0.6× 461 1.5× 302 1.2× 151 1.9k
G. Valli Italy 15 244 0.6× 231 0.6× 114 0.4× 331 1.1× 204 0.8× 41 928
Luiz Otávio Murta Brazil 16 170 0.4× 222 0.6× 88 0.3× 102 0.3× 124 0.5× 78 882
Muthu Rama Krishnan Mookiah Singapore 23 220 0.5× 210 0.5× 76 0.2× 597 2.0× 219 0.9× 50 1.9k
Ruxin Wang China 21 215 0.5× 550 1.4× 376 1.2× 207 0.7× 200 0.8× 76 1.4k
Ali Ghaffari Iran 17 97 0.2× 614 1.6× 418 1.3× 104 0.3× 320 1.3× 81 1.1k

Countries citing papers authored by Tomasz Markiewicz

Since Specialization
Citations

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

Fields of papers citing papers by Tomasz Markiewicz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tomasz Markiewicz

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

All Works

20 of 20 papers shown
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Szymański, Łukasz, Sławomir Lewicki, Tomasz Markiewicz, et al.. (2023). siRNA-Mediated MELK Knockdown Induces Accelerated Wound Healing with Increased Collagen Deposition. International Journal of Molecular Sciences. 24(2). 1326–1326. 4 indexed citations
4.
Markiewicz, Tomasz, et al.. (2023). Localization of spleen and kidney organs from CT scans based on classification of slices in rotational views. Scientific Reports. 13(1). 5709–5709. 1 indexed citations
5.
Markiewicz, Tomasz, et al.. (2022). Improvement of renal image recognition through resolution enhancement. Expert Systems with Applications. 213. 118836–118836. 7 indexed citations
6.
Gallego, Jaime, et al.. (2021). A U-Net based framework to quantify glomerulosclerosis in digitized PAS and H&E stained human tissues. Computerized Medical Imaging and Graphics. 89. 101865–101865. 23 indexed citations
7.
Gertych, Arkadiusz, Żaneta Świderska-Chadaj, Zhaoxuan Ma, et al.. (2019). Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides. Scientific Reports. 9(1). 1483–1483. 119 indexed citations
8.
Świderska-Chadaj, Żaneta, et al.. (2018). Deep learning for damaged tissue detection and segmentation in Ki-67 brain tumor specimens based on the U-net model. Bulletin of the Polish Academy of Sciences Technical Sciences. 849–856. 20 indexed citations
9.
Słodkowska, J, Szczepan Cierniak, Włodzimierz Baranowski, et al.. (2016). Functional Assessment of Synoptic Pathology Reporting for Ovarian Cancer. Pathobiology. 83(2-3). 70–78. 11 indexed citations
10.
Świderska-Chadaj, Żaneta, et al.. (2016). Content-based analysis of Ki-67 stained meningioma specimens for automatic hot-spot selection. Diagnostic Pathology. 11(1). 93–93. 12 indexed citations
11.
Markiewicz, Tomasz, et al.. (2014). Recognition of atherosclerotic plaques and their extended dimensioning with computerized tomography angiography imaging. International Journal of Applied Mathematics and Computer Science. 24(1). 33–47. 3 indexed citations
12.
Kruk, Michał, S. Osowski, Wojciech Kozłowski, et al.. (2013). Computer-assisted Fuhrman grading system for the analysis of clear-cell renal carcinoma: a pilot study. PRZEGLĄD ELEKTROTECHNICZNY. 1 indexed citations
13.
Siwek, Krzysztof, et al.. (2013). Analysis of medical data using dimensionality reduction techniques. PRZEGLĄD ELEKTROTECHNICZNY. 89. 4 indexed citations
14.
Markiewicz, Tomasz, et al.. (2012). Alternative colour space construction of the stain reaction in FISH image analysis for quantification of the HER2 gene amplification. PRZEGLĄD ELEKTROTECHNICZNY. 10–12. 1 indexed citations
15.
Papierz, W, et al.. (2010). New automated image analysis method for the assessment of Ki-67 labeling index in meningiomas.. SHILAP Revista de lepidopterología. 3 indexed citations
16.
Grala, Bartłomiej, Tomasz Markiewicz, Wojciech Kozłowski, et al.. (2010). New automated image analysis method for the assessment of Ki-67 labeling index in meningiomas.. Folia Histochemica et Cytobiologica. 47(4). 587–92. 34 indexed citations
17.
Markiewicz, Tomasz, et al.. (2009). Application of SVM for cell recognition in BCC skin pathology.. The European Symposium on Artificial Neural Networks.
18.
Markiewicz, Tomasz & S. Osowski. (2008). Morphological operations for blood cells extraction from the image of the bone marrow smear. PRZEGLĄD ELEKTROTECHNICZNY. 24–26. 1 indexed citations
19.
Markiewicz, Tomasz & S. Osowski. (2006). Data mining techniques for feature selection in blood cell recognition. The European Symposium on Artificial Neural Networks. 407–412. 12 indexed citations
20.
Osowski, S., Krzysztof Siwek, & Tomasz Markiewicz. (2004). MLP and SVM networks - a comparative study. 37–40. 51 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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