Ramona Woitek

3.6k total citations
88 papers, 2.0k citations indexed

About

Ramona Woitek is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pathology and Forensic Medicine. According to data from OpenAlex, Ramona Woitek has authored 88 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 59 papers in Radiology, Nuclear Medicine and Imaging, 17 papers in Artificial Intelligence and 14 papers in Pathology and Forensic Medicine. Recurrent topics in Ramona Woitek's work include Radiomics and Machine Learning in Medical Imaging (26 papers), MRI in cancer diagnosis (25 papers) and AI in cancer detection (16 papers). Ramona Woitek is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (26 papers), MRI in cancer diagnosis (25 papers) and AI in cancer detection (16 papers). Ramona Woitek collaborates with scholars based in Austria, United Kingdom and Italy. Ramona Woitek's co-authors include Panagiotis Kapetas, Pascal Baltzer, Thomas H. Helbich, Katja Pinker, Daniela Prayer, Julia Furtner, Paola Clauser, Ferdia A. Gallagher, Ulrika Asenbaum and Veronika Schöpf and has published in prestigious journals such as Journal of Clinical Oncology, PLoS ONE and Scientific Reports.

In The Last Decade

Ramona Woitek

83 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ramona Woitek Austria 26 1.1k 276 248 230 203 88 2.0k
Kenji Hirata Japan 27 1.5k 1.3× 200 0.7× 651 2.6× 320 1.4× 135 0.7× 186 2.9k
Benjamin L. Franc United States 26 1.2k 1.0× 208 0.8× 472 1.9× 356 1.5× 72 0.4× 108 2.6k
Sebastian Bickelhaupt Germany 29 1.9k 1.7× 186 0.7× 464 1.9× 301 1.3× 96 0.5× 99 2.8k
Anton S. Becker Switzerland 28 1.4k 1.2× 488 1.8× 955 3.9× 357 1.6× 137 0.7× 135 3.0k
Grace Hyun J. Kim United States 27 785 0.7× 133 0.5× 974 3.9× 128 0.6× 470 2.3× 88 2.2k
Alexander Sauter Germany 27 1.5k 1.3× 161 0.6× 487 2.0× 536 2.3× 44 0.2× 115 2.8k
Augustin Lecler France 20 670 0.6× 154 0.6× 436 1.8× 170 0.7× 221 1.1× 113 1.7k
Mohamed Houseni United States 27 720 0.6× 61 0.2× 468 1.9× 102 0.4× 104 0.5× 70 1.8k
Felix Nensa Germany 30 1.8k 1.6× 255 0.9× 638 2.6× 299 1.3× 76 0.4× 167 2.9k
Ji Soo Choi South Korea 26 1.4k 1.2× 424 1.5× 351 1.4× 233 1.0× 349 1.7× 106 2.9k

Countries citing papers authored by Ramona Woitek

Since Specialization
Citations

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

Fields of papers citing papers by Ramona Woitek

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ramona Woitek

This figure shows the co-authorship network connecting the top 25 collaborators of Ramona Woitek. A scholar is included among the top collaborators of Ramona Woitek 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 Ramona Woitek. Ramona Woitek 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.
2.
Whitmarsh, Tristan, Wei Cope, Roido Manavaki, et al.. (2025). Quantifying the tumour vasculature environment from CD-31 immunohistochemistry images of breast cancer using deep learning based semantic segmentation. Breast Cancer Research. 27(1). 17–17.
3.
Mahbod, Amirreza, et al.. (2024). NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images. Scientific Data. 11(1). 295–295. 14 indexed citations
4.
Avesani, Giacomo, Camilla Panico, Stéphanie Nougaret, et al.. (2024). ESR Essentials: characterisation and staging of adnexal masses with MRI and CT—practice recommendations by ESUR. European Radiology. 34(12). 7673–7689. 5 indexed citations
5.
6.
Mahbod, Amirreza, Georg Dorffner, Isabella Ellinger, Ramona Woitek, & Sepideh Hatamikia. (2024). Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization. Computational and Structural Biotechnology Journal. 23. 669–678. 6 indexed citations
7.
Biguri, Ander, Giovanni Di Domenico, Cristina Sarti, et al.. (2024). Source-detector trajectory optimization for FOV extension in dental CBCT imaging. Computational and Structural Biotechnology Journal. 24. 679–689. 1 indexed citations
8.
Lightowlers, Sara, Ramona Woitek, Elena Provenzano, et al.. (2024). Neoadjuvant Radiotherapy and Endocrine Therapy for Oestrogen Receptor Positive Breast Cancers: The Neo-RT Feasibility Study. Clinical Oncology. 37. 103669–103669. 2 indexed citations
9.
Ursprung, Stephan & Ramona Woitek. (2023). The Steep Road to Artificial Intelligence–mediated Radiology. Radiology Artificial Intelligence. 5(2). e230017–e230017. 3 indexed citations
10.
Escudero, L., Mohammad Al Sa’d, Cathal McCague, et al.. (2023). Integrating Artificial Intelligence Tools in the Clinical Research Setting: The Ovarian Cancer Use Case. Diagnostics. 13(17). 2813–2813. 2 indexed citations
11.
Manavaki, Roido, Jodi L. Miller, Corradina Caracò, et al.. (2023). PET/MRI of hypoxia and vascular function in ER-positive breast cancer: correlations with immunohistochemistry. European Radiology. 33(9). 6168–6178. 3 indexed citations
12.
Reinius, Marika, Cathal McCague, Vlad Bura, et al.. (2023). Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study. Frontiers in Oncology. 13. 1085874–1085874. 2 indexed citations
13.
Romeo, Valeria, Panagiotis Kapetas, Paola Clauser, et al.. (2022). A Simultaneous Multiparametric 18F-FDG PET/MRI Radiomics Model for the Diagnosis of Triple Negative Breast Cancer. Cancers. 14(16). 3944–3944. 11 indexed citations
14.
Panico, Camilla, Ramona Woitek, Anna D’Angelo, et al.. (2022). Staging Breast Cancer with MRI, the T. A Key Role in the Neoadjuvant Setting. Cancers. 14(23). 5786–5786. 12 indexed citations
15.
Eijnatten, Maureen van, Leonardo Rundo, Kees Joost Batenburg, et al.. (2021). 3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning. Data Archiving and Networked Services (DANS). 10 indexed citations
16.
Hickman, Sarah, Ramona Woitek, Elizabeth Le, et al.. (2021). Machine Learning for Workflow Applications in Screening Mammography: Systematic Review and Meta-Analysis. Radiology. 302(1). 88–104. 82 indexed citations
17.
Kapetas, Panagiotis, Paola Clauser, Ramona Woitek, et al.. (2018). Virtual Touch IQ elastography reduces unnecessary breast biopsies by applying quantitative “rule-in” and “rule-out” threshold values. Scientific Reports. 8(1). 3583–3583. 5 indexed citations
18.
Nolz, Richard, Ulrika Asenbaum, Julia Furtner, et al.. (2016). Type 2 Endoleaks: The Diagnostic Performance of Non-Specialized Readers on Arterial and Venous Phase Multi-Slice CT Angiography. PLoS ONE. 11(3). e0149725–e0149725. 2 indexed citations
19.
Michaely, Henrik J., Manuela Aschauer, Hannes Deutschmann, et al.. (2016). Gadobutrol in Renally Impaired Patients. Investigative Radiology. 52(1). 55–60. 41 indexed citations
20.
Furtner, Julia, Veronika Schöpf, Gregor Kasprian, et al.. (2013). Arterial Spin-Labeling Assessment of Normalized Vascular Intratumoral Signal Intensity as a Predictor of Histologic Grade of Astrocytic Neoplasms. American Journal of Neuroradiology. 35(3). 482–489. 21 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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