Maschenka Balkenhol

5.3k total citations
21 papers, 906 citations indexed

About

Maschenka Balkenhol is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Maschenka Balkenhol has authored 21 papers receiving a total of 906 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 9 papers in Radiology, Nuclear Medicine and Imaging and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Maschenka Balkenhol's work include AI in cancer detection (17 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and Breast Cancer Treatment Studies (7 papers). Maschenka Balkenhol is often cited by papers focused on AI in cancer detection (17 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and Breast Cancer Treatment Studies (7 papers). Maschenka Balkenhol collaborates with scholars based in Netherlands, Sweden and Germany. Maschenka Balkenhol's co-authors include Jeroen van der Laak, Geert Litjens, Peter Bult, Francesco Ciompi, Babak Ehteshami Bejnordi, Mart van Rijthoven, Meyke Hermsen, Nico Karssemeijer, Karīna Siliņa and Bram van Ginneken and has published in prestigious journals such as IEEE Transactions on Medical Imaging, Breast Cancer Research and Medical Image Analysis.

In The Last Decade

Maschenka Balkenhol

20 papers receiving 885 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maschenka Balkenhol Netherlands 13 685 530 298 151 139 21 906
Simon Graham United Kingdom 14 770 1.1× 590 1.1× 389 1.3× 183 1.2× 154 1.1× 28 1.0k
Péter Bándi Netherlands 8 617 0.9× 421 0.8× 292 1.0× 116 0.8× 125 0.9× 15 779
Guillaume Jaume United States 11 577 0.8× 401 0.8× 210 0.7× 114 0.8× 107 0.8× 14 885
Nikolas Stathonikos Netherlands 14 508 0.7× 328 0.6× 163 0.5× 124 0.8× 108 0.8× 32 687
Wouter Bulten Netherlands 8 752 1.1× 530 1.0× 275 0.9× 157 1.0× 103 0.7× 9 998
Hans Pinckaers Netherlands 12 608 0.9× 477 0.9× 183 0.6× 150 1.0× 76 0.5× 20 899
Thomas de Bel Netherlands 10 641 0.9× 459 0.9× 231 0.8× 159 1.1× 91 0.7× 16 1.0k
Chengkuan Chen United States 4 488 0.7× 372 0.7× 177 0.6× 81 0.5× 68 0.5× 5 807
N. K. Timofeeva Netherlands 4 717 1.0× 471 0.9× 292 1.0× 137 0.9× 143 1.0× 10 963
Ruchika Verma United States 10 583 0.9× 499 0.9× 463 1.6× 79 0.5× 161 1.2× 30 1.0k

Countries citing papers authored by Maschenka Balkenhol

Since Specialization
Citations

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

Fields of papers citing papers by Maschenka Balkenhol

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maschenka Balkenhol

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

All Works

20 of 20 papers shown
1.
Leon‐Ferre, Roberto A., Jodi M. Carter, David M. Zahrieh, et al.. (2024). Automated mitotic spindle hotspot counts are highly associated with clinical outcomes in systemically untreated early-stage triple-negative breast cancer. npj Breast Cancer. 10(1). 25–25. 1 indexed citations
2.
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
3.
Munari, Enrico, Hugo M. Horlings, Lennart Mulder, et al.. (2023). PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning. Breast Cancer Research. 25(1). 142–142. 10 indexed citations
4.
Balkenhol, Maschenka, Annick Haesevoets, Michelene N. Chenault, et al.. (2022). Evaluation Criteria for Chromosome Instability Detection by FISH to Predict Malignant Progression in Premalignant Glottic Laryngeal Lesions. Cancers. 14(13). 3260–3260.
5.
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
6.
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
7.
Balkenhol, Maschenka, Francesco Ciompi, Żaneta Świderska-Chadaj, et al.. (2021). Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics. The Breast. 56. 78–87. 24 indexed citations
8.
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
9.
Balkenhol, Maschenka, et al.. (2020). Histological subtypes in triple negative breast cancer are associated with specific information on survival. Annals of Diagnostic Pathology. 46. 151490–151490. 26 indexed citations
10.
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
11.
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
12.
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
13.
Balkenhol, Maschenka, Peter Bult, David Tellez, et al.. (2019). Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer. Cellular Oncology. 42(4). 555–569. 17 indexed citations
14.
Balkenhol, Maschenka, et al.. (2019). From Point Annotations to Epithelial Cell Detection in Breast Cancer Histopathology using RetinaNet. 1 indexed citations
15.
Balkenhol, Maschenka, Nico Karssemeijer, Geert Litjens, et al.. (2018). H&E stain augmentation improves generalization of convolutional networks for histopathological mitosis detection. 34–34. 16 indexed citations
16.
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
17.
Zelst, Jan van, Maschenka Balkenhol, Tao Tan, et al.. (2017). Sonographic Phenotypes of Molecular Subtypes of Invasive Ductal Cancer in Automated 3-D Breast Ultrasound. Ultrasound in Medicine & Biology. 43(9). 1820–1828. 8 indexed citations
18.
Bejnordi, Babak Ehteshami, Guido Zuidhof, Maschenka Balkenhol, et al.. (2017). Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images. Journal of Medical Imaging. 4(4). 1–1. 130 indexed citations
19.
Homeyer, André, et al.. (2016). A generic nuclei detection method for histopathological breast images. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9791. 97911E–97911E. 4 indexed citations
20.
Bejnordi, Babak Ehteshami, Maschenka Balkenhol, Geert Litjens, et al.. (2016). Automated Detection of DCIS in Whole-Slide H&E Stained Breast Histopathology Images. IEEE Transactions on Medical Imaging. 35(9). 2141–2150. 63 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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