John‐Melle Bokhorst

1.1k total citations · 1 hit paper
22 papers, 617 citations indexed

About

John‐Melle Bokhorst is a scholar working on Oncology, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, John‐Melle Bokhorst has authored 22 papers receiving a total of 617 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Oncology, 14 papers in Radiology, Nuclear Medicine and Imaging and 12 papers in Artificial Intelligence. Recurrent topics in John‐Melle Bokhorst's work include Radiomics and Machine Learning in Medical Imaging (13 papers), AI in cancer detection (12 papers) and Colorectal Cancer Screening and Detection (9 papers). John‐Melle Bokhorst is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (13 papers), AI in cancer detection (12 papers) and Colorectal Cancer Screening and Detection (9 papers). John‐Melle Bokhorst collaborates with scholars based in Netherlands, Switzerland and Sweden. John‐Melle Bokhorst's co-authors include Jeroen van der Laak, Francesco Ciompi, Geert Litjens, Péter Bándi, Wouter Bulten, David Tellez, Irıs D. Nagtegaal, Thomas de Bel, Inti Zlobec and Alessandro Lugli and has published in prestigious journals such as PLoS ONE, Scientific Reports and European Journal of Cancer.

In The Last Decade

John‐Melle Bokhorst

21 papers receiving 607 citations

Hit Papers

align trajectories

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.

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						Papers	Cites
John‐Melle Bokhorst Netherlands	13	402	282	201	192	74	22	617
Żaneta Świderska-Chadaj Poland	13	360 0.9×	273 1.0×	141 0.7×	103 0.5×	83 1.1×	30	582
David Tellez Netherlands	7	480 1.2×	310 1.1×	243 1.2×	95 0.5×	84 1.1×	9	592
Marcial García‐Rojo Spain	15	349 0.9×	181 0.6×	155 0.8×	114 0.6×	153 2.1×	57	682
Zeyan Xu China	13	301 0.7×	440 1.6×	149 0.7×	180 0.9×	23 0.3×	42	758
Norman Zerbe Germany	12	501 1.2×	314 1.1×	194 1.0×	118 0.6×	96 1.3×	34	780
Amelie Echle Germany	9	362 0.9×	350 1.2×	67 0.3×	198 1.0×	73 1.0×	10	618
Maschenka Balkenhol Netherlands	13	685 1.7×	530 1.9×	298 1.5×	151 0.8×	139 1.9×	21	906
Chengkuan Chen United States	4	488 1.2×	372 1.3×	177 0.9×	81 0.4×	68 0.9×	5	807
Andrew Zhang United States	4	362 0.9×	265 0.9×	120 0.6×	84 0.4×	68 0.9×	6	619
Nikolas Stathonikos Netherlands	14	508 1.3×	328 1.2×	163 0.8×	124 0.6×	108 1.5×	32	687

Countries citing papers authored by John‐Melle Bokhorst

Since Specialization

Citations

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

Fields of papers citing papers by John‐Melle Bokhorst

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John‐Melle Bokhorst. 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 John‐Melle Bokhorst. The network helps show where John‐Melle Bokhorst may publish in the future.

Co-authorship network of co-authors of John‐Melle Bokhorst

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

All Works

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20 of 20 papers shown

Bokhorst, John‐Melle, Shannon van Vliet, Kiek Verrijp, et al.. (2025). Tumor budding and poorly differentiated clusters as a biological continuum in colorectal cancer invasion and prognosis. Scientific Reports. 15(1). 16944–16944.

Bontkes, Hetty J., Elske C. Gootjes, Martine Reijm, et al.. (2024). Exploring immune status in peripheral blood and tumor tissue in association with survival in patients with multi-organ metastatic colorectal cancer. OncoImmunology. 13(1). 2361971–2361971. 3 indexed citations

Bokhorst, John‐Melle, Martin D. Berger, Femke Simmer, et al.. (2024). Combining immunoscore and tumor budding in colon cancer: an insightful prognostication based on the tumor-host interface. Journal of Translational Medicine. 22(1). 1090–1090. 4 indexed citations

Dawson, Heather, John‐Melle Bokhorst, Michael Vieth, et al.. (2024). Lymph node metastases and recurrence in pT1 colorectal cancer: Prediction with the International Budding Consortium Score—A retrospective, multi‐centric study. United European Gastroenterology Journal. 12(3). 299–308. 4 indexed citations

Bokhorst, John‐Melle, et al.. (2024). Color Deconvolution for Color-Agnostic and Cross-Modality Analysis of Immunohistochemistry Whole-Slide Images with Deep Learning. 1–4. 1 indexed citations

Bokhorst, John‐Melle, Kieran Sheahan, Cornelis J. H. van de Velde, et al.. (2024). Exploring Intratumoral Budding in Colorectal Cancer Using Computational Pathology: A Biopsy-Based Evaluation. Modern Pathology. 38(2). 100655–100655. 2 indexed citations

Bokhorst, John‐Melle, E. Smeets, Valentyna Kryklyva, et al.. (2024). Automatic quantification of tumor-stroma ratio as a prognostic marker for pancreatic cancer. PLoS ONE. 19(5). e0301969–e0301969. 3 indexed citations

Brouwer, Nelleke P.M., Amjad Khan, John‐Melle Bokhorst, et al.. (2023). The Complexity of Shapes: How the Circularity of Tumor Nodules Affects Prognosis in Colorectal Cancer. Modern Pathology. 37(1). 100376–100376. 3 indexed citations

Ciompi, Francesco, John‐Melle Bokhorst, Gabi W. van Pelt, et al.. (2023). Deep learning based tumor–stroma ratio scoring in colon cancer correlates with microscopic assessment. Journal of Pathology Informatics. 14. 100191–100191. 15 indexed citations

10.

Bokhorst, John‐Melle, Irıs D. Nagtegaal, Inti Zlobec, et al.. (2023). Semi-Supervised Learning to Automate Tumor Bud Detection in Cytokeratin-Stained Whole-Slide Images of Colorectal Cancer. Cancers. 15(7). 2079–2079. 12 indexed citations

11.

Bokhorst, John‐Melle, Irıs D. Nagtegaal, Filippo Fraggetta, et al.. (2023). Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images. Scientific Reports. 13(1). 8398–8398. 26 indexed citations

12.

Bokhorst, John‐Melle, Francesco Ciompi, Michael Vieth, et al.. (2023). Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer. Modern Pathology. 36(9). 100233–100233. 15 indexed citations

13.

Balkenhol, Maschenka, Roberto Salgado, Mark E. Sherman, et al.. (2022). Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer. npj Breast Cancer. 8(1). 120–120. 12 indexed citations

14.

Marini, Niccolò, Stefano Marchesin, Sebastian Otálora, et al.. (2022). Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations. npj Digital Medicine. 5(1). 102–102. 40 indexed citations

15.

Bel, Thomas de, John‐Melle Bokhorst, Jeroen van der Laak, & Geert Litjens. (2021). Residual cyclegan for robust domain transformation of histopathological tissue slides. Medical Image Analysis. 70. 102004–102004. 59 indexed citations

16.

Blank, Annika, John‐Melle Bokhorst, Irıs D. Nagtegaal, et al.. (2020). Taking tumour budding to the next frontier — a post International Tumour Budding Consensus Conference (ITBCC) 2016 review. Histopathology. 78(4). 476–484. 19 indexed citations

17.

Zlobec, Inti, Heather Dawson, Annika Blank, et al.. (2020). Are tumour grade and tumour budding equivalent in colorectal cancer? A retrospective analysis of 771 patients. European Journal of Cancer. 130. 139–145. 14 indexed citations

18.

Bokhorst, John‐Melle, Annika Blank, Alessandro Lugli, et al.. (2019). Assessment of individual tumor buds using keratin immunohistochemistry: moderate interobserver agreement suggests a role for machine learning. Modern Pathology. 33(5). 825–833. 34 indexed citations

19.

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 →

20.

Bokhorst, John‐Melle, Berit M. Verbist, Jean‐Pierre Bayley, et al.. (2018). Mathematical Models for Tumor Growth and the Reduction of Overtreatment. Journal of Neurological Surgery Part B Skull Base. 80(1). 72–78. 14 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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