Péter Bándi

1.9k total citations · 1 hit paper
15 papers, 779 citations indexed

About

Péter Bándi is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Péter Bándi has authored 15 papers receiving a total of 779 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Artificial Intelligence and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Péter Bándi's work include Radiomics and Machine Learning in Medical Imaging (8 papers), AI in cancer detection (8 papers) and Medical Imaging Techniques and Applications (4 papers). Péter Bándi is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (8 papers), AI in cancer detection (8 papers) and Medical Imaging Techniques and Applications (4 papers). Péter Bándi collaborates with scholars based in Netherlands, Germany and Hungary. Péter Bándi's co-authors include Jeroen van der Laak, Geert Litjens, Wouter Bulten, Francesco Ciompi, John‐Melle Bokhorst, David Tellez, Rob van de Loo, Bram van Ginneken, Maschenka Balkenhol and Marcory van Dijk and has published in prestigious journals such as Scientific Reports, Journal of the American Society of Nephrology and American Journal Of Pathology.

In The Last Decade

Péter Bándi

14 papers receiving 764 citations

Hit Papers

Quantifying the effects of data augmentation and stain co... 2019 2026 2021 2023 2019 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
    2019 317 citations David Tellez, Geert Litjens et al. Medical Image Analysis profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Péter Bándi Netherlands 8 617 421 292 125 116 15 779
Alexi Baidoshvili Netherlands 15 492 0.8× 346 0.8× 193 0.7× 113 0.9× 149 1.3× 22 920
Maschenka Balkenhol Netherlands 13 685 1.1× 530 1.3× 298 1.0× 139 1.1× 151 1.3× 21 906
Nikolas Stathonikos Netherlands 14 508 0.8× 328 0.8× 163 0.6× 108 0.9× 124 1.1× 32 687
N. K. Timofeeva Netherlands 4 717 1.2× 471 1.1× 292 1.0× 143 1.1× 137 1.2× 10 963
Norman Zerbe Germany 12 501 0.8× 314 0.7× 194 0.7× 96 0.8× 118 1.0× 34 780
Guillaume Jaume United States 11 577 0.9× 401 1.0× 210 0.7× 107 0.9× 114 1.0× 14 885
Iringo Kovacs Netherlands 5 538 0.9× 386 0.9× 181 0.6× 85 0.7× 105 0.9× 7 762
Simon Graham United Kingdom 14 770 1.2× 590 1.4× 389 1.3× 154 1.2× 183 1.6× 28 1.0k
David Tellez Netherlands 7 480 0.8× 310 0.7× 243 0.8× 84 0.7× 95 0.8× 9 592
Ruchika Verma United States 10 583 0.9× 499 1.2× 463 1.6× 161 1.3× 79 0.7× 30 1.0k

Countries citing papers authored by Péter Bándi

Since Specialization
Citations

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

Fields of papers citing papers by Péter Bándi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Péter Bándi

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

All Works

15 of 15 papers shown
1.
Bándi, Péter, Maschenka Balkenhol, Marcory van Dijk, et al.. (2023). Continual learning strategies for cancer-independent detection of lymph node metastases. Medical Image Analysis. 85. 102755–102755. 18 indexed citations
2.
Bándi, Péter, et al.. (2022). Generalization of Deep Learning in Digital Pathology: Experience in Breast Cancer Metastasis Detection. Cancers. 14(21). 5424–5424. 19 indexed citations
3.
Hermsen, Meyke, Francesco Ciompi, Aleksandar Đenić, et al.. (2022). Convolutional Neural Networks for the Evaluation of Chronic and Inflammatory Lesions in Kidney Transplant Biopsies. American Journal Of Pathology. 192(10). 1418–1432. 21 indexed citations
4.
Broek, Martijn van den, Bartholomeus T. van den Berge, Fieke Mooren, et al.. (2022). Human Scattered Tubular Cells Represent a Heterogeneous Population of Glycolytic Dedifferentiated Proximal Tubule Cells. Journal of the American Society of Nephrology. 33(11S). 198–198.
5.
Bándi, Péter, Brigith Willemsen, Fieke Mooren, et al.. (2021). Parietal epithelial cells maintain the epithelial cell continuum forming Bowman's space in focal segmental glomerulosclerosis. Disease Models & Mechanisms. 15(3). 5 indexed citations
6.
Bándi, Péter, Maschenka Balkenhol, Bram van Ginneken, Jeroen van der Laak, & Geert Litjens. (2019). Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks. PeerJ. 7. e8242–e8242. 38 indexed citations
7.
Bulten, Wouter, Péter Bándi, Rob van de Loo, et al.. (2019). Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard. Scientific Reports. 9(1). 864–864. 101 indexed citations
8.
Tellez, David, Geert Litjens, Péter Bándi, et al.. (2019). Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. Medical Image Analysis. 58. 101544–101544. 317 indexed citations breakdown →
9.
Litjens, Geert, Péter Bándi, Babak Ehteshami Bejnordi, et al.. (2018). 1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset. GigaScience. 7(6). 216 indexed citations
10.
Bándi, Péter, Rob van de Loo, Francesco Ciompi, et al.. (2017). Comparison of different methods for tissue segmentation in histopathological whole-slide images. 591–595. 29 indexed citations
11.
Zsótér, Norbert, Péter Bándi, Zoltán Tóth, et al.. (2012). PET-CT based automated lung nodule detection. PubMed. 51. 4974–4977. 5 indexed citations
12.
Bándi, Péter, et al.. (2012). New workflows and algorithms of bone scintigraphy based on SPECT-CT. PubMed. 42. 5971–5974. 3 indexed citations
13.
Bándi, Péter, et al.. (2012). Automated material map generation from MRI scout pairs for preclinical PET attenuation correction. PubMed. 4. 5351–5354. 4 indexed citations
14.
Bándi, Péter, Norbert Zsótér, L. Serés, Zoltán Tóth, & László Papp. (2011). Automated patient couch removal algorithm on CT images. PubMed. 7. 7783–7786. 2 indexed citations
15.
Zsótér, Norbert, et al.. (2011). Hextuple registration of interim and follow-up PET-CT images for the accurate tracking of patient recovery after therapy. PubMed. 3. 2630–2633. 1 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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