Marek Wodziński

1.2k total citations
48 papers, 469 citations indexed

About

Marek Wodziński is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Marek Wodziński has authored 48 papers receiving a total of 469 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 20 papers in Radiology, Nuclear Medicine and Imaging and 12 papers in Computer Vision and Pattern Recognition. Recurrent topics in Marek Wodziński's work include AI in cancer detection (18 papers), Radiomics and Machine Learning in Medical Imaging (14 papers) and Digital Imaging for Blood Diseases (6 papers). Marek Wodziński is often cited by papers focused on AI in cancer detection (18 papers), Radiomics and Machine Learning in Medical Imaging (14 papers) and Digital Imaging for Blood Diseases (6 papers). Marek Wodziński collaborates with scholars based in Poland, Switzerland and Italy. Marek Wodziński's co-authors include Andrzej Skalski, Daria Hemmerling, Henning Müller, Juan Rafael Orozco‐Arroyave, Elmar Nöth, Manfredo Atzori, Tommaso Banzato, Alessandro Zotti, Krzysztof R. Apt and Joanna Łudzik and has published in prestigious journals such as Scientific Reports, Sensors and Physics in Medicine and Biology.

In The Last Decade

Marek Wodziński

43 papers receiving 454 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marek Wodziński Poland 12 193 163 81 75 66 48 469
Yeşim Eroğlu Türkiye 9 126 0.7× 131 0.8× 66 0.8× 24 0.3× 31 0.5× 23 343
Zhanhao Mo China 13 271 1.4× 436 2.7× 103 1.3× 19 0.3× 49 0.7× 28 805
Michael Gadermayr Austria 11 234 1.2× 149 0.9× 159 2.0× 12 0.2× 42 0.6× 37 461
Matej Gazda Slovakia 9 125 0.6× 60 0.4× 54 0.7× 113 1.5× 15 0.2× 16 356
Bibo Shi United States 10 151 0.8× 159 1.0× 75 0.9× 9 0.1× 29 0.4× 25 375
Chirag Agarwal United States 11 118 0.6× 65 0.4× 68 0.8× 20 0.3× 26 0.4× 45 424
Themis P. Exarchos Greece 12 92 0.5× 190 1.2× 26 0.3× 46 0.6× 123 1.9× 41 563
Soroosh Tayebi Arasteh Germany 10 185 1.0× 217 1.3× 84 1.0× 16 0.2× 35 0.5× 25 455
Sarfaraz Hussein United States 7 206 1.1× 248 1.5× 79 1.0× 36 0.5× 43 0.7× 12 458
Tahir Mahmood South Korea 13 232 1.2× 342 2.1× 192 2.4× 25 0.3× 77 1.2× 30 620

Countries citing papers authored by Marek Wodziński

Since Specialization
Citations

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

Fields of papers citing papers by Marek Wodziński

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marek Wodziński

This figure shows the co-authorship network connecting the top 25 collaborators of Marek Wodziński. A scholar is included among the top collaborators of Marek Wodziński 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 Marek Wodziński. Marek Wodziński 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.
2.
Hemmerling, Daria, et al.. (2025). Gait analysis in mixed reality for Parkinson’s disease assessment. Biomedical Signal Processing and Control. 106. 107659–107659.
3.
Nurzyǹska, Karolina, et al.. (2025). Automated determination of hip arthrosis on the Kellgren–Lawrence scale in pelvic digital radiographs scans using machine learning. Computer Methods and Programs in Biomedicine. 266. 108742–108742. 2 indexed citations
4.
Cepeda, Santiago, O Esteban Sinovas, Luigi Tommaso Luppino, et al.. (2025). Radiomics-based quantification of tumor infiltration in the non-enhancing peritumoral region on postoperative MRI is associated with survival in glioblastoma. Scientific Reports. 15(1). 43932–43932.
5.
Obuchowicz, Rafał, et al.. (2025). Artificial Intelligence-Empowered Radiology—Current Status and Critical Review. Diagnostics. 15(3). 282–282. 19 indexed citations
6.
Schiappacasse, Luis, Daniel Abler, Marek Wodziński, et al.. (2024). Automatic detection and multi-component segmentation of brain metastases in longitudinal MRI. Scientific Reports. 14(1). 31603–31603. 1 indexed citations
7.
Ibragimov, Bulat, C. Lee, Jin Sung Kim, et al.. (2024). HaN-Seg: The head and neck organ-at-risk CT and MR segmentation challenge. Radiotherapy and Oncology. 198. 110410–110410. 6 indexed citations
8.
Marini, Niccolò, Stefano Marchesin, Marek Wodziński, et al.. (2024). Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning. Medical Image Analysis. 97. 103303–103303. 4 indexed citations
9.
Wodziński, Marek, et al.. (2024). Improving deep learning-based automatic cranial defect reconstruction by heavy data augmentation: From image registration to latent diffusion models. Computers in Biology and Medicine. 182. 109129–109129. 6 indexed citations
10.
Wodziński, Marek, et al.. (2023). DRU-Net: Pulmonary Artery Segmentation via Dense Residual U-Network with Hybrid Loss Function. Sensors. 23(12). 5427–5427. 6 indexed citations
11.
Banzato, Tommaso, et al.. (2023). An AI-based algorithm for the automatic evaluation of image quality in canine thoracic radiographs. Scientific Reports. 13(1). 17024–17024. 4 indexed citations
12.
13.
Wodziński, Marek, et al.. (2022). Deep learning-based framework for automatic cranial defect reconstruction and implant modeling. Computer Methods and Programs in Biomedicine. 226. 107173–107173. 18 indexed citations
14.
Wodziński, Marek, et al.. (2021). Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization. Sensors. 21(12). 4085–4085. 12 indexed citations
15.
Banzato, Tommaso, et al.. (2021). Automatic classification of canine thoracic radiographs using deep learning. Scientific Reports. 11(1). 3964–3964. 44 indexed citations
16.
Wodziński, Marek & Andrzej Skalski. (2020). Multistep, automatic and nonrigid image registration method for histology samples acquired using multiple stains. Physics in Medicine and Biology. 66(2). 25006–25006. 11 indexed citations
17.
Wodziński, Marek, et al.. (2020). Contact-Free Multispectral Identity Verification System Using Palm Veins and Deep Neural Network. Sensors. 20(19). 5695–5695. 7 indexed citations
18.
Wodziński, Marek & Henning Müller. (2020). DeepHistReg: Unsupervised Deep Learning Registration Framework for Differently Stained Histology Samples. Computer Methods and Programs in Biomedicine. 198. 105799–105799. 20 indexed citations
19.
Wodziński, Marek, et al.. (2018). Improving oncoplastic breast tumor bed localization for radiotherapy planning using image registration algorithms. Physics in Medicine and Biology. 63(3). 35024–35024. 8 indexed citations
20.
Apt, Krzysztof R. & Marek Wodziński. (1974). Second order arithmetic and related topics. Annals of Mathematical Logic. 6(3-4). 177–229. 21 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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