Hans Pinckaers

1.6k total citations · 1 hit paper
20 papers, 899 citations indexed

About

Hans Pinckaers is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Hans Pinckaers has authored 20 papers receiving a total of 899 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 10 papers in Radiology, Nuclear Medicine and Imaging and 8 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Hans Pinckaers's work include AI in cancer detection (14 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Prostate Cancer Diagnosis and Treatment (7 papers). Hans Pinckaers is often cited by papers focused on AI in cancer detection (14 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Prostate Cancer Diagnosis and Treatment (7 papers). Hans Pinckaers collaborates with scholars based in Netherlands, United States and Sweden. Hans Pinckaers's co-authors include Geert Litjens, Jeroen van der Laak, Bram van Ginneken, Wouter Bulten, Thomas de Bel, Robert Vink, Hester van Boven, Christina Hulsbergen‐van de Kaa, Francesco Ciompi and Maschenka Balkenhol and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Hans Pinckaers

20 papers receiving 890 citations

Hit Papers

align trajectories sort by overperformance

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.

Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study

2020 421 citations Wouter Bulten, Hans Pinckaers et al. The Lancet Oncology profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Hans Pinckaers	608	477	217	183	150	20	899
Thomas de Bel	641 1.1×	459 1.0×	245 1.1×	231 1.3×	159 1.1×	16	1.0k
Wouter Bulten	752 1.2×	530 1.1×	210 1.0×	275 1.5×	157 1.0×	9	998
Fahdi Kanavati	500 0.8×	504 1.1×	168 0.8×	122 0.7×	218 1.5×	20	794
Lily H. Peng	446 0.7×	376 0.8×	134 0.6×	96 0.5×	93 0.6×	6	689
Anurag Vaidya	480 0.8×	407 0.9×	83 0.4×	117 0.6×	104 0.7×	10	865
Jeremias Krause	466 0.8×	489 1.0×	106 0.5×	99 0.5×	305 2.0×	8	868
Alexi Baidoshvili	492 0.8×	346 0.7×	89 0.4×	193 1.1×	149 1.0×	22	920
Pierre Courtiol	436 0.7×	418 0.9×	113 0.5×	70 0.4×	164 1.1×	7	815
Matahi Moarii	434 0.7×	422 0.9×	144 0.7×	70 0.4×	215 1.4×	12	1.1k
Dyke Ferber	579 1.0×	507 1.1×	74 0.3×	138 0.8×	386 2.6×	23	1.1k

Countries citing papers authored by Hans Pinckaers

Since Specialization

Citations

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

Fields of papers citing papers by Hans Pinckaers

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hans Pinckaers

This figure shows the co-authorship network connecting the top 25 collaborators of Hans Pinckaers. A scholar is included among the top collaborators of Hans Pinckaers 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 Hans Pinckaers. Hans Pinckaers 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

Kreipe, Hans, Oleg Gluz, Matthias Christgen, et al.. (2024). Multimodal artificial intelligence models from baseline histopathology to predict prognosis in HR+ HER2- early breast cancer: Subgroup analysis.. Journal of Clinical Oncology. 42(16_suppl). 101–101. 2 indexed citations

Pinckaers, Hans, et al.. (2023). Gigapixel end-to-end training using streaming and attention. Medical Image Analysis. 88. 102881–102881. 9 indexed citations

Holste, Gregory, et al.. (2023). Improved Multimodal Fusion for Small Datasets with Auxiliary Supervision. 1–5. 2 indexed citations

Pinckaers, Hans, Jonathan Melamed, Angelo M. De Marzo, et al.. (2022). Predicting biochemical recurrence of prostate cancer with artificial intelligence. SHILAP Revista de lepidopterología. 2(1). 64–64. 23 indexed citations

Pinckaers, Hans. (2022). Source code for "Predicting biochemical recurrence of prostate cancer with artificial intelligence". Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

Kartasalo, Kimmo, Wouter Bulten, Brett Delahunt, et al.. (2021). Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer in Biopsies—Current Status and Next Steps. European Urology Focus. 7(4). 687–691. 31 indexed citations

Pinckaers, Hans, et al.. (2021). Using deep learning for quantification of cellularity and cell lineages in bone marrow biopsies and comparison to normal age-related variation. Pathology. 54(3). 318–327. 15 indexed citations

Pinckaers, Hans, et al.. (2021). End-to-end classification on basal-cell carcinoma histopathology whole-slides images. 4–4. 4 indexed citations

Bulten, Wouter, Hans Pinckaers, Hester van Boven, et al.. (2020). Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. arXiv (Cornell University). 77 indexed citations

10.

Pinckaers, Hans, Bram van Ginneken, & Geert Litjens. (2020). Streaming Convolutional Neural Networks for End-to-End Learning With Multi-Megapixel Images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(3). 1581–1590. 58 indexed citations

11.

Bulten, Wouter, Geert Litjens, Hans Pinckaers, et al.. (2020). The PANDA challenge: Prostate cANcer graDe Assessment using the Gleason grading system. Zenodo (CERN European Organization for Nuclear Research). 14 indexed citations

12.

Bulten, Wouter, Hans Pinckaers, Hester van Boven, et al.. (2020). Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. The Lancet Oncology. 21(2). 233–241. 421 indexed citations breakdown →

13.

Balkenhol, Maschenka, David Tellez, Willem Vreuls, et al.. (2019). Deep learning assisted mitotic counting for breast cancer. Laboratory Investigation. 99(11). 1596–1606. 73 indexed citations

14.

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

15.

Pinckaers, Hans, Wouter Bulten, & Geert Litjens. (2019). High resolution whole prostate biopsy classification using streaming stochastic gradient descent. 8–8. 1 indexed citations

16.

Kolk, Tessa Niemeyer‐van der, Hans Pinckaers, Jan Nico Bouwes Bavinck, et al.. (2019). Mobile e‐diary application facilitates the monitoring of patient‐reported outcomes and a high treatment adherence for clinical trials in dermatology. Journal of the European Academy of Dermatology and Venereology. 34(3). 633–639. 15 indexed citations

17.

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

18.

Bokhorst, John‐Melle, et al.. (2018). Learning from sparsely annotated data for semantic segmentation in histopathology images. 84–91. 12 indexed citations

19.

Jong, Ype de, Hans Pinckaers, Robin M. ten Brinck, A.A.B. Lycklama à Nijeholt, & Olaf M. Dekkers. (2014). Urinating Standing versus Sitting: Position Is of Influence in Men with Prostate Enlargement. A Systematic Review and Meta-Analysis. PLoS ONE. 9(7). e101320–e101320. 26 indexed citations

20.

Jong, Ype de, Hans Pinckaers, Robin M. ten Brinck, & A.A.B. Lycklama à Nijeholt. (2014). Invloed van mictiehouding op urodynamische parameters bij mannen: een literatuuronderzoek. 4(1). 36–42. 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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