Siri Willems

1.1k total citations · 1 hit paper
14 papers, 616 citations indexed

About

Siri Willems is a scholar working on Radiology, Nuclear Medicine and Imaging, Radiation and Otorhinolaryngology. According to data from OpenAlex, Siri Willems has authored 14 papers receiving a total of 616 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Radiation and 4 papers in Otorhinolaryngology. Recurrent topics in Siri Willems's work include Radiomics and Machine Learning in Medical Imaging (10 papers), Advanced Radiotherapy Techniques (7 papers) and Head and Neck Cancer Studies (4 papers). Siri Willems is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (10 papers), Advanced Radiotherapy Techniques (7 papers) and Head and Neck Cancer Studies (4 papers). Siri Willems collaborates with scholars based in Belgium, Netherlands and Sweden. Siri Willems's co-authors include Frederik Maes, Sandra Nuyts, Liesbeth Vandewinckele, Julie van der Veen, Kevin Souris, Gilmer Valdés, Edmond Sterpin, Dan Nguyen, John A. Lee and Ana María Barragán Montero and has published in prestigious journals such as International Journal of Radiation Oncology*Biology*Physics, Physics in Medicine and Biology and Human Pathology.

In The Last Decade

Siri Willems

14 papers receiving 599 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.

Artificial intelligence and machine learning for medical imaging: A technology review

2021 236 citations Ana María Barragán Montero, Umair Javaid et al. Physica Medica profile →

Peers

Siri Willems

RNMI

RADIA

AI

BE

PRM

Fredrik Löfman Sweden

H. Geng United States

Carlotta Masciocchi Italy

Dong Joo Rhee United States

Jason Xie Canada

Liesbeth Vandewinckele Belgium

Seyed Masoud Rezaeijo Iran

Pierre Decazes France

Elisa Scalco Italy

Steven Michiels Belgium

Fredrik Löfman Sweden

Siri Willems

264 ×0.8

RNMI

119 ×0.6

RADIA

115 ×0.7

AI

81 ×0.8

BE

73 ×0.8

PRM

Citations per year, relative to Siri Willems Siri Willems (= 1×) peers Fredrik Löfman

Countries citing papers authored by Siri Willems

Since Specialization

Citations

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

Fields of papers citing papers by Siri Willems

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Siri Willems

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

All Works

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14 of 14 papers shown

1.

Willems, Siri, et al.. (2023). Benefits of automated gross tumor volume segmentation in head and neck cancer using multi-modality information. Radiotherapy and Oncology. 182. 109574–109574. 20 indexed citations

2.

Montero, Ana María Barragán, Adrien Bibal, Gilmer Valdés, et al.. (2022). Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency. Physics in Medicine and Biology. 67(11). 11TR01–11TR01. 48 indexed citations

3.

Willems, Siri, et al.. (2022). Clinical evaluation of a deep learning model for segmentation of target volumes in breast cancer radiotherapy. Radiotherapy and Oncology. 171. 84–90. 14 indexed citations

4.

Vandewinckele, Liesbeth, Siri Willems, Maarten Lambrecht, et al.. (2022). Treatment plan prediction for lung IMRT using deep learning based fluence map generation. Physica Medica. 99. 44–54. 17 indexed citations

5.

Montero, Ana María Barragán, Umair Javaid, Gilmer Valdés, et al.. (2021). Artificial intelligence and machine learning for medical imaging: A technology review. Physica Medica. 83. 242–256. 236 indexed citations breakdown →

6.

Veen, Julie van der, Ákos Gulybán, Siri Willems, Frederik Maes, & Sandra Nuyts. (2021). Interobserver variability in organ at risk delineation in head and neck cancer. Radiation Oncology. 16(1). 45 indexed citations

7.

Veen, Julie van der, et al.. (2020). Deep learning for elective neck delineation: More consistent and time efficient. Radiotherapy and Oncology. 153. 180–188. 24 indexed citations

8.

Willems, Siri, et al.. (2020). Clinical Evaluation of a Deep Learning Model for Segmentation of Nodal Clinical Target Volumes in Breast Cancer Radiotherapy. International Journal of Radiation Oncology*Biology*Physics. 108(3). S101–S102. 1 indexed citations

9.

Veen, Julie van der, Siri Willems, David Robben, et al.. (2019). Benefits of deep learning for delineation of organs at risk in head and neck cancer. Radiotherapy and Oncology. 138. 68–74. 78 indexed citations

10.

Vandewinckele, Liesbeth, Siri Willems, David Robben, et al.. (2019). Segmentation of head-and-neck organs-at-risk in longitudinal CT scans combining deformable registrations and convolutional neural networks. Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization. 8(5). 519–528. 13 indexed citations

11.

Willems, Siri, et al.. (2017). PO-0621: Validation of tumor delineation on HE stained sections with cytokeratin staining as gold standard. Radiotherapy and Oncology. 123. S324–S325. 1 indexed citations

12.

Bree, Remco de, et al.. (2017). [Cutting through tumour in head and neck cancer: still a taboo?]. PubMed. 161. D1327–D1327. 1 indexed citations

13.

Al‐Janabi, Shaimaa, A.M. Huisman, Siri Willems, & P. J. van Diest. (2012). Digital slide images for primary diagnostics in breast pathology: a feasibility study. Human Pathology. 43(12). 2318–2325. 48 indexed citations

14.

Pieterse, Q. D., Gemma G. Kenter, Katja N. Gaarenstroom, et al.. (2006). The number of pelvic lymph nodes in the quality control and prognosis of radical hysterectomy for the treatment of cervical cancer. European Journal of Surgical Oncology. 33(2). 216–221. 70 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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