Mart van Rijthoven

551 total citations
8 papers, 338 citations indexed

About

Mart van Rijthoven is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Mart van Rijthoven has authored 8 papers receiving a total of 338 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 4 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Mart van Rijthoven's work include AI in cancer detection (7 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Digital Imaging for Blood Diseases (4 papers). Mart van Rijthoven is often cited by papers focused on AI in cancer detection (7 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Digital Imaging for Blood Diseases (4 papers). Mart van Rijthoven collaborates with scholars based in Netherlands, Sweden and Switzerland. Mart van Rijthoven's co-authors include Francesco Ciompi, Jeroen van der Laak, Maschenka Balkenhol, Karīna Siliņa, Żaneta Świderska-Chadaj, Hans Pinckaers, Geert Litjens, Oscar Geessink, Quirine F. Manson and Mustapha Abubakar and has published in prestigious journals such as SHILAP Revista de lepidopterología, Medical Image Analysis and npj Digital Medicine.

In The Last Decade

Mart van Rijthoven

8 papers receiving 326 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mart van Rijthoven Netherlands 7 231 169 116 76 53 8 338
Nick Weiss Germany 10 176 0.8× 151 0.9× 104 0.9× 49 0.6× 53 1.0× 16 353
Susanne Melchers Germany 8 290 1.3× 199 1.2× 141 1.2× 126 1.7× 32 0.6× 17 493
Shahira Abousamra United States 9 221 1.0× 110 0.7× 141 1.2× 55 0.7× 54 1.0× 23 324
Tianhao Zhao China 7 233 1.0× 163 1.0× 94 0.8× 56 0.7× 57 1.1× 13 349
Shazia Akbar United Kingdom 8 150 0.6× 116 0.7× 104 0.9× 34 0.4× 50 0.9× 20 269
Zhaoxuan Ma United States 8 254 1.1× 182 1.1× 84 0.7× 64 0.8× 53 1.0× 9 377
Rob van de Loo Netherlands 5 297 1.3× 212 1.3× 126 1.1× 49 0.6× 71 1.3× 5 379
Quirine F. Manson Netherlands 8 265 1.1× 195 1.2× 116 1.0× 132 1.7× 66 1.2× 9 490
Mostafa Jahanifar United Kingdom 10 256 1.1× 177 1.0× 109 0.9× 87 1.1× 45 0.8× 20 376
Aïcha BenTaieb Canada 8 331 1.4× 161 1.0× 168 1.4× 198 2.6× 53 1.0× 11 495

Countries citing papers authored by Mart van Rijthoven

Since Specialization
Citations

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

Fields of papers citing papers by Mart van Rijthoven

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mart van Rijthoven. 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 Mart van Rijthoven. The network helps show where Mart van Rijthoven may publish in the future.

Co-authorship network of co-authors of Mart van Rijthoven

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

All Works

8 of 8 papers shown
1.
Rijthoven, Mart van, Maries van den Broek, Peter Schraml, et al.. (2024). Multi-resolution deep learning characterizes tertiary lymphoid structures and their prognostic relevance in solid tumors. SHILAP Revista de lepidopterología. 4(1). 5–5. 21 indexed citations
2.
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
3.
Rijthoven, Mart van, Maschenka Balkenhol, Karīna Siliņa, Jeroen van der Laak, & Francesco Ciompi. (2021). HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images. Data Archiving and Networked Services (DANS). 134 indexed citations
4.
Rijthoven, Mart van, Maschenka Balkenhol, Manfredo Atzori, et al.. (2021). Few-shot weakly supervised detection and retrieval in histopathology whole-slide images. Research Padua Archive (University of Padua). 20–20. 1 indexed citations
5.
Marini, Niccolò, Sebastian Otálora, Damian Podareanu, et al.. (2021). Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images. Frontiers in Computer Science. 3. 14 indexed citations
6.
Świderska-Chadaj, Żaneta, Hans Pinckaers, Mart van Rijthoven, et al.. (2019). Learning to detect lymphocytes in immunohistochemistry with deep learning. Medical Image Analysis. 58. 101547–101547. 104 indexed citations
7.
Rijthoven, Mart van, Żaneta Świderska-Chadaj, Katja Seeliger, Jeroen van der Laak, & Francesco Ciompi. (2018). You Only Look on Lymphocytes Once. 14 indexed citations
8.
Świderska-Chadaj, Żaneta, Hans Pinckaers, Mart van Rijthoven, et al.. (2018). Convolutional Neural Networks for Lymphocyte detection in Immunohistochemically Stained Whole-Slide Images. 10 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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