Dawid Rymarczyk

496 total citations
9 papers, 161 citations indexed

About

Dawid Rymarczyk is a scholar working on Artificial Intelligence, Biophysics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Dawid Rymarczyk has authored 9 papers receiving a total of 161 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 4 papers in Biophysics and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Dawid Rymarczyk's work include Cell Image Analysis Techniques (4 papers), Image Processing Techniques and Applications (3 papers) and AI in cancer detection (2 papers). Dawid Rymarczyk is often cited by papers focused on Cell Image Analysis Techniques (4 papers), Image Processing Techniques and Applications (3 papers) and AI in cancer detection (2 papers). Dawid Rymarczyk collaborates with scholars based in Poland, Belgium and Spain. Dawid Rymarczyk's co-authors include Bartosz Zieliński, Monika Brzychczy‐Włoch, Agnieszka Sroka-Oleksiak, Jacek Tabor, Łukasz Struski, Joost van de Weijer, Bartłomiej Twardowski, Katherine Li, Gert De Hertogh and Joshua R. Friedman and has published in prestigious journals such as PLoS ONE, IEEE Journal of Biomedical and Health Informatics and Journal of Crohn s and Colitis.

In The Last Decade

Dawid Rymarczyk

8 papers receiving 158 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dawid Rymarczyk Poland	5	64	39	32	31	25	9	161
Michael B. Mayhew United States	11	37 0.6×	9 0.2×	10 0.3×	9 0.3×	8 0.3×	16	343
Aman Rana United States	6	75 1.2×	35 0.9×	38 1.2×	34 1.1×	8 0.3×	16	185
Hassaan Maan Canada	7	41 0.6×	10 0.3×	75 2.3×	28 0.9×	14 0.6×	9	480
Ankit Jaiswal India	9	20 0.3×	119 3.1×	25 0.8×	6 0.2×	8 0.3×	32	241
Tim Scherr Germany	7	57 0.9×	57 1.5×	70 2.2×	36 1.2×	7 0.3×	17	156
Tathagat Banerjee India	7	25 0.4×	47 1.2×	5 0.2×	24 0.8×	16 0.6×	21	105
Longxi Zhou Saudi Arabia	7	130 2.0×	34 0.9×	3 0.1×	150 4.8×	22 0.9×	13	328
Redha Ali United States	9	85 1.3×	109 2.8×	15 0.5×	82 2.6×	23 0.9×	15	219
Xiao Jian Tan Malaysia	9	86 1.3×	30 0.8×	20 0.6×	77 2.5×	9 0.4×	33	209

Countries citing papers authored by Dawid Rymarczyk

Since Specialization

Citations

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

Fields of papers citing papers by Dawid Rymarczyk

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dawid Rymarczyk

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

All Works

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9 of 9 papers shown

Rymarczyk, Dawid, et al.. (2024). Decoding phenotypic screening: A comparative analysis of image representations. Computational and Structural Biotechnology Journal. 23. 1181–1188.

Rymarczyk, Dawid, et al.. (2024). Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanations. Proceedings of the AAAI Conference on Artificial Intelligence. 38(19). 21563–21573. 3 indexed citations

Rymarczyk, Dawid, Joshua R. Friedman, Tomasz Danel, et al.. (2023). Deep Learning Models Capture Histological Disease Activity in Crohn’s Disease and Ulcerative Colitis with High Fidelity. Journal of Crohn s and Colitis. 18(4). 604–614. 27 indexed citations

Rymarczyk, Dawid, et al.. (2023). ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 1481–1492. 22 indexed citations

Rymarczyk, Dawid, Joost van de Weijer, Bartosz Zieliński, & Bartłomiej Twardowski. (2023). ICICLE: Interpretable Class Incremental Continual Learning. Jagiellonian University Repository (Jagiellonian University). 1887–1898. 14 indexed citations

Rymarczyk, Dawid, et al.. (2022). Identifying Bacteria Species on Microscopic Polyculture Images Using Deep Learning. IEEE Journal of Biomedical and Health Informatics. 27(1). 121–130. 1 indexed citations

Rymarczyk, Dawid, et al.. (2021). Kernel Self-Attention for Weakly-supervised Image Classification using Deep Multiple Instance Learning. Jagiellonian University Repository (Jagiellonian University). 1720–1729. 26 indexed citations

Rymarczyk, Dawid, et al.. (2021). Deep learning classification of bacteria clones explained by persistence homology. Jagiellonian University Repository (Jagiellonian University). 8. 1–8. 4 indexed citations

Zieliński, Bartosz, et al.. (2020). Deep learning approach to describe and classify fungi microscopic images. PLoS ONE. 15(6). e0234806–e0234806. 64 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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