Geert Litjens

32.8k total citations · 9 hit papers
101 papers, 16.1k citations indexed

About

Geert Litjens is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Geert Litjens has authored 101 papers receiving a total of 16.1k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Artificial Intelligence, 49 papers in Radiology, Nuclear Medicine and Imaging and 35 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Geert Litjens's work include AI in cancer detection (64 papers), Radiomics and Machine Learning in Medical Imaging (36 papers) and Prostate Cancer Diagnosis and Treatment (30 papers). Geert Litjens is often cited by papers focused on AI in cancer detection (64 papers), Radiomics and Machine Learning in Medical Imaging (36 papers) and Prostate Cancer Diagnosis and Treatment (30 papers). Geert Litjens collaborates with scholars based in Netherlands, United States and Sweden. Geert Litjens's co-authors include Jeroen van der Laak, Francesco Ciompi, Bram van Ginneken, Clara I. Sá‎nchez, Arnaud A. A. Setio, Babak Ehteshami Bejnordi, Thijs Kooi, Mohsen Ghafoorian, Nico Karssemeijer and Jelle O. Barentsz and has published in prestigious journals such as Nature Medicine, SHILAP Revista de lepidopterología and Gastroenterology.

In The Last Decade

Geert Litjens

97 papers receiving 15.7k citations

Hit Papers

5 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.

A survey on deep learning in medical image analysis

2017 8.3k citations Geert Litjens, Thijs Kooi et al. Medical Image Analysis profile →
Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks

2016 863 citations Arnaud A. A. Setio, Francesco Ciompi et al. profile →
Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

2016 722 citations Geert Litjens, Clara I. Sá‎nchez et al. profile →
Large scale deep learning for computer aided detection of mammographic lesions

2016 676 citations Thijs Kooi, Geert Litjens et al. Medical Image Analysis profile →
Deep learning in histopathology: the path to the clinic

2021 509 citations Jeroen van der Laak, Geert Litjens et al. profile →
Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study

2020 421 citations Wouter Bulten, Hans Pinckaers et al. profile →
The 2019 International Society of Urological Pathology (ISUP) Consensus Conference on Grading of Prostatic Carcinoma

2020 388 citations Geert Litjens et al. profile →
Computer-Aided Detection of Prostate Cancer in MRI

2014 334 citations Geert Litjens, Oscar A. Debats et al. profile →
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

2019 317 citations Geert Litjens, Péter Bándi et al. Medical Image Analysis profile →

Peers

Countries citing papers authored by Geert Litjens

Since Specialization

Citations

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

Fields of papers citing papers by Geert Litjens

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Geert Litjens

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work	Indexed citations
1	Hierarchical Vision Transformers for prostate biopsy grading: Towards bridging the generalization gap 2025 ·Medical Image Analysis ·(unknown), Kimmo Kartasalo, Martin Eklund, Lars Egevad, Jeroen van der Laak, Geert Litjens	0
2	Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications 2025 ·Medical Image Analysis ·(unknown), (unknown), (unknown), (unknown), (unknown), (unknown), Geert Litjens, Nadieh Khalili	9
3	Transformer-Based Weakly Supervised Learning for Whole Slide Lung Cancer Image Classification 2024 ·IEEE Journal of Biomedical and Health Informatics ·(unknown), Yong Wang, Qing Cai, Gang Zhao, (unknown), Geert Litjens, Zhongke Gao	6
4	Weakly Supervised Classification of Mohs Surgical Sections Using Artificial Intelligence 2024 ·Modern Pathology ·(unknown), Lisa M. Hillen, (unknown), Véronique Winnepenninckx, (unknown), David R. Carr, (unknown), Geert Litjens, Avital L. Amir	2
5	Artificial Intelligence in Pancreatic Ductal Adenocarcinoma Imaging: A Commentary on Potential Future Applications 2023 ·Gastroenterology ·(unknown), (unknown), (unknown), Henkjan Huisman, John J. Hermans, Geert Litjens, David K. Chang, Caroline S. Verbeke, Núria Malats, Matthias Löhr	10
6	Continual learning strategies for cancer-independent detection of lymph node metastases 2023 ·Medical Image Analysis ·Péter Bándi, Maschenka Balkenhol, Marcory van Dijk, (unknown), Bram van Ginneken, Jeroen van der Laak, Geert Litjens	18
7	Gigapixel end-to-end training using streaming and attention 2023 ·Medical Image Analysis ·(unknown), Hans Pinckaers, (unknown), Konnie M. Hebeda, (unknown), Jeroen van der Laak, Geert Litjens	9
8	PythoStitcher: an iterative approach for stitching digitized tissue fragments into full resolution whole-mount reconstructions 2023 ·(unknown), Geert Litjens	0
9	Prostate158 - An expert-annotated 3T MRI dataset and algorithm for prostate cancer detection 2022 ·Computers in Biology and Medicine ·Lisa C. Adams, Marcus R. Makowski, Günther Engel, (unknown), Felix Busch, Patrick Asbach, Stefan M. Niehues, Shankeeth Vinayahalingam, Bram van Ginneken, Geert Litjens, Keno K. Bressem	44
10	Generalization of Deep Learning in Digital Pathology: Experience in Breast Cancer Metastasis Detection 2022 ·Cancers ·(unknown), (unknown), (unknown), (unknown), Péter Bándi, Geert Litjens, Claes Lundström, Darren Treanor, Jeroen van der Laak	19
11	Mini Review: The Last Mile—Opportunities and Challenges for Machine Learning in Digital Toxicologic Pathology 2021 ·Toxicologic Pathology ·Oliver C. Turner, Brian L. Knight, (unknown), Geert Litjens, (unknown)	8
12	Tailoring automated data augmentation to H&E-stained histopathology 2021 ·(unknown), Jeroen van der Laak, Geert Litjens	15
13	Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks 2020 ·Jasper Linmans, Jeroen van der Laak, Geert Litjens	10
14	Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study 2020 ·arXiv (Cornell University) ·Wouter Bulten, Hans Pinckaers, Hester van Boven, Robert Vink, Thomas de Bel, Bram van Ginneken, Jeroen van der Laak, Christina A. Hulsbergen ‐ van de Kaa, Geert Litjens	77
15	Learning to detect lymphocytes in immunohistochemistry with deep learning 2019 ·Medical Image Analysis ·Żaneta Świderska-Chadaj, Hans Pinckaers, Mart van Rijthoven, Maschenka Balkenhol, (unknown), Oscar Geessink, Quirine F. Manson, Mark E. Sherman, António Polónia, (unknown), Mustapha Abubakar, Geert Litjens, Jeroen van der Laak, Francesco Ciompi	104
16	Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks 2019 ·PeerJ ·Péter Bándi, Maschenka Balkenhol, Bram van Ginneken, Jeroen van der Laak, Geert Litjens	38
17	Automatic color unmixing of IHC stained whole slide images 2018 ·(unknown), (unknown), Geert Litjens, Jeroen van der Laak	11
18	The importance of stain normalization in colorectal tissue classification with convolutional networks 2017 ·Francesco Ciompi, Oscar Geessink, Babak Ehteshami Bejnordi, (unknown), Alexi Baidoshvili, Geert Litjens, Bram van Ginneken, Irıs D. Nagtegaal, Jeroen van der Laak	128
19	A survey on deep learning in medical image analysis breakdown → 2017 ·Medical Image Analysis ·Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud A. A. Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen van der Laak, Bram van Ginneken, Clara I. Sá‎nchez	8341
20	A pattern recognition approach to zonal segmentation of the prostate on MRI 2012 ·Lecture notes in computer science ·Geert Litjens, Oscar A. Debats, Wendy van de Ven, Nico Karssemeijer, Henkjan Huisman	21

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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