Vassili Kovalev

5.2k total citations
59 papers, 830 citations indexed

About

Vassili Kovalev is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Vassili Kovalev has authored 59 papers receiving a total of 830 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Computer Vision and Pattern Recognition, 18 papers in Artificial Intelligence and 17 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Vassili Kovalev's work include Medical Image Segmentation Techniques (14 papers), AI in cancer detection (14 papers) and Radiomics and Machine Learning in Medical Imaging (10 papers). Vassili Kovalev is often cited by papers focused on Medical Image Segmentation Techniques (14 papers), AI in cancer detection (14 papers) and Radiomics and Machine Learning in Medical Imaging (10 papers). Vassili Kovalev collaborates with scholars based in Belarus, United Kingdom and Germany. Vassili Kovalev's co-authors include Frithjof Kruggel, D. Yves von Cramon, Μαρία Πέτρου, H.-J. Gertz, M. Petrou, Mehdi Alilou, Alexander Kalinovsky, Achim Köhler, Volha Shapaval and Hyo-Sok Ahn and has published in prestigious journals such as SHILAP Revista de lepidopterología, Biomaterials and NeuroImage.

In The Last Decade

Vassili Kovalev

52 papers receiving 780 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vassili Kovalev Belarus 15 339 264 186 108 89 59 830
Jialin Peng China 16 457 1.3× 390 1.5× 329 1.8× 62 0.6× 54 0.6× 40 1.0k
Andrea U. J. Mewes United States 8 417 1.2× 278 1.1× 150 0.8× 50 0.5× 43 0.5× 8 878
Sokratis Makrogiannis United States 16 759 2.2× 125 0.5× 123 0.7× 85 0.8× 39 0.4× 69 1.4k
Bilge Karaçalı United States 16 387 1.1× 233 0.9× 188 1.0× 153 1.4× 56 0.6× 58 888
Baohua Wu China 11 77 0.2× 224 0.8× 99 0.5× 138 1.3× 61 0.7× 21 888
S. Sandor-Leahy United States 3 450 1.3× 316 1.2× 62 0.3× 134 1.2× 34 0.4× 9 770
Yueyang Teng China 16 266 0.8× 613 2.3× 295 1.6× 98 0.9× 51 0.6× 78 1.1k
Ahmed Shalaby United States 21 288 0.8× 673 2.5× 256 1.4× 284 2.6× 20 0.2× 84 1.5k
Mingxia Liu China 19 348 1.0× 377 1.4× 430 2.3× 257 2.4× 29 0.3× 44 1.6k
A. F. C. Infantosi Brazil 15 233 0.7× 313 1.2× 370 2.0× 252 2.3× 15 0.2× 63 911

Countries citing papers authored by Vassili Kovalev

Since Specialization
Citations

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

Fields of papers citing papers by Vassili Kovalev

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vassili Kovalev

This figure shows the co-authorship network connecting the top 25 collaborators of Vassili Kovalev. A scholar is included among the top collaborators of Vassili Kovalev 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 Vassili Kovalev. Vassili Kovalev 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
1.
Ye, Mang, Shen Wei, Bo Du, et al.. (2025). Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey. ACM Computing Surveys. 57(9). 1–32. 7 indexed citations
2.
Zhang, Lihong, Hanhui Li, Ning Tang, et al.. (2024). Cell calcification reverses the chemoresistance of cancer cells via the conversion of glycolipid metabolism. Biomaterials. 314. 122886–122886. 2 indexed citations
3.
Shapaval, Volha, et al.. (2021). Comparison of augmentation and pre-processing for deep learning and chemometric classification of infrared spectra. Chemometrics and Intelligent Laboratory Systems. 215. 104367–104367. 64 indexed citations
4.
Liauchuk, Vitali, et al.. (2021). Overview of ImageCLEFtuberculosis 2021 - CT-based Tuberculosis Type Classification. ArODES (HES-SO (https://www.hes-so.ch/)). 1089–1100. 3 indexed citations
5.
Liauchuk, Vitali, et al.. (2020). Overview of ImageCLEFtuberculosis 2020 Automatic CT-based Report Generation.. ArODES (HES-SO (https://www.hes-so.ch/)). 1 indexed citations
6.
Kovalev, Vassili, et al.. (2020). Development of neoplastic region selection algorithm based on breast cancer whole slide image. SHILAP Revista de lepidopterología. 18(8). 21–28.
7.
Cid, Yashin Dicente, et al.. (2019). Overview of ImageCLEFtuberculosis 2019 - Automatic CT-based Report Generation and Tuberculosis Severity Assessment.. CLEF (Working Notes). 16 indexed citations
8.
Kovalev, Vassili, et al.. (2019). Experimental assessment of аdversarial attacks to the deep neural networks in medical image recognition. SHILAP Revista de lepidopterología. 1 indexed citations
9.
Cid, Yashin Dicente, Vitali Liauchuk, Vassili Kovalev, & Henning Müller. (2018). Overview of ImageCLEFtuberculosis 2018 - Detecting Multi-Drug Resistance, Classifying Tuberculosis Types and Assessing Severity Scores.. CLEF (Working Notes). 11 indexed citations
10.
Liauchuk, Vitali, et al.. (2018). ImageCLEF 2018: Lesion-based TB-descriptor for CT Image Analysis.. CLEF (Working Notes). 4 indexed citations
11.
Cid, Yashin Dicente, Alexander Kalinovsky, Vitali Liauchuk, Vassili Kovalev, & Henning Müller. (2017). Overview of the ImageCLEF 2017 Tuberculosis Task - Predicting Tuberculosis Type and Drug Resistances.. CLEF (Working Notes). 5 indexed citations
12.
Petrou, M., Farahnaz Mohanna, & Vassili Kovalev. (2004). 3D non-linear invisible boundary detection filters. 970–978.
13.
Pagani, Marco, Vassili Kovalev, Roger Lundqvist, et al.. (2003). A new approach for improving diagnostic accuracy in Alzheimer?s disease and frontal lobe dementia utilising the intrinsic properties of the SPET dataset. European Journal of Nuclear Medicine and Molecular Imaging. 30(11). 1481–1488. 5 indexed citations
14.
Kovalev, Vassili, Maria Petrou, & John Suckling. (2003). Detection of structural differences between the brains of schizophrenic patients and controls. Psychiatry Research Neuroimaging. 124(3). 177–189. 18 indexed citations
15.
Kruggel, Frithjof & Vassili Kovalev. (2002). Classifying brain shapes. 2678–2682 vol.5. 1 indexed citations
16.
Kovalev, Vassili & M. Petrou. (1998). Non-rigid Volume Registration of Medical Images. Hrčak Portal of scientific journals of Croatia (University Computing Centre). 6(2). 181–190. 5 indexed citations
17.
Kovalev, Vassili, et al.. (1998). Using orientation tokens for object recognition. Pattern Recognition Letters. 19(12). 1125–1132. 9 indexed citations
18.
Kovalev, Vassili, et al.. (1997). 3D Surface roughness quantification.. 83(11). 3839–44. 2 indexed citations
19.
Kovalev, Vassili, et al.. (1995). <title>Automatic localization and feature extraction of white blood cells</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 2434. 754–765. 10 indexed citations
20.
Зайцева, О. В., et al.. (1982). A study of the cerebral region of the visual system in the pulmonata. Neurophysiology. 14(2). 144–148. 3 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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