Katharina Hoebel

956 total citations
17 papers, 472 citations indexed

About

Katharina Hoebel is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Biomedical Engineering. According to data from OpenAlex, Katharina Hoebel has authored 17 papers receiving a total of 472 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Radiology, Nuclear Medicine and Imaging, 6 papers in Artificial Intelligence and 5 papers in Biomedical Engineering. Recurrent topics in Katharina Hoebel's work include Radiomics and Machine Learning in Medical Imaging (8 papers), AI in cancer detection (5 papers) and Glioma Diagnosis and Treatment (3 papers). Katharina Hoebel is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (8 papers), AI in cancer detection (5 papers) and Glioma Diagnosis and Treatment (3 papers). Katharina Hoebel collaborates with scholars based in United States, Switzerland and India. Katharina Hoebel's co-authors include Jayashree Kalpathy–Cramer, Ken Chang, Praveer Singh, Matthew Li, Jay Patel, Nathan Gaw, Nishanth Arun, Mishka Gidwani, James M. Brown and Jay Patel and has published in prestigious journals such as Radiology, IEEE Transactions on Medical Imaging and Neuro-Oncology.

In The Last Decade

Katharina Hoebel

14 papers receiving 466 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Katharina Hoebel United States 10 237 157 81 77 70 17 472
Natascha Claudia D’Amico Italy 7 305 1.3× 198 1.3× 99 1.2× 73 0.9× 80 1.1× 10 538
Manudeep Kalra United States 11 208 0.9× 87 0.6× 96 1.2× 48 0.6× 45 0.6× 14 380
Paul Desbordes France 9 285 1.2× 142 0.9× 71 0.9× 104 1.4× 57 0.8× 13 534
Iñaki Soto‐Rey Germany 7 219 0.9× 143 0.9× 63 0.8× 51 0.7× 28 0.4× 28 521
Ryoungwoo Jang South Korea 7 214 0.9× 124 0.8× 113 1.4× 49 0.6× 49 0.7× 8 398
Mohamed Shehata Egypt 14 377 1.6× 152 1.0× 107 1.3× 142 1.8× 61 0.9× 55 648
Gopichandh Danala United States 11 447 1.9× 342 2.2× 51 0.6× 123 1.6× 53 0.8× 35 654
Dominik Müller Germany 6 270 1.1× 173 1.1× 80 1.0× 58 0.8× 30 0.4× 16 540
Avi Ben-Cohen Israel 8 262 1.1× 209 1.3× 118 1.5× 68 0.9× 79 1.1× 10 594
Alanna Vial Australia 5 242 1.0× 117 0.7× 87 1.1× 62 0.8× 84 1.2× 8 440

Countries citing papers authored by Katharina Hoebel

Since Specialization
Citations

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

Fields of papers citing papers by Katharina Hoebel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Katharina Hoebel

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

All Works

17 of 17 papers shown
1.
Altreuter, Jennifer, Joao V. Alessi, Jason L. Weirather, et al.. (2025). Pan-cancer spatial characterization of key immune biomarkers in the tumor microenvironment. Cell Reports Medicine. 102418–102418.
2.
Hoebel, Katharina, Christopher P. Bridge, Sara Ahmed, et al.. (2023). Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation. Radiology Artificial Intelligence. 6(1). e220231–e220231. 4 indexed citations
3.
Hoebel, Katharina, Christopher P. Bridge, Albert Kim, et al.. (2023). Not without Context—A Multiple Methods Study on Evaluation and Correction of Automated Brain Tumor Segmentations by Experts. Academic Radiology. 31(4). 1572–1582. 3 indexed citations
4.
Kim, Albert, Jay Patel, William J. Liu, et al.. (2023). NIMG-76. A DEEP LEARNING ALGORITHM FOR FULLY AUTOMATED VOLUMETRIC MEASUREMENT OF MENINGIOMA BURDEN. Neuro-Oncology. 25(Supplement_5). v203–v203.
5.
Deng, Bin, Hanxue Gu, Ken Chang, et al.. (2023). FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction. IEEE Transactions on Medical Imaging. 42(8). 2439–2450. 13 indexed citations
6.
Gidwani, Mishka, Ken Chang, Jay Patel, et al.. (2022). Inconsistent Partitioning and Unproductive Feature Associations Yield Idealized Radiomic Models. Radiology. 307(1). e220715–e220715. 21 indexed citations
7.
Hoebel, Katharina, Christopher P. Bridge, Brian Befano, et al.. (2022). Improving the repeatability of deep learning models with Monte Carlo dropout. npj Digital Medicine. 5(1). 174–174. 41 indexed citations
8.
Hoebel, Katharina, et al.. (2022). Do I know this? segmentation uncertainty under domain shift. 27–27. 1 indexed citations
9.
Arun, Nishanth, Nathan Gaw, Praveer Singh, et al.. (2021). Assessing the Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging. Radiology Artificial Intelligence. 3(6). e200267–e200267. 147 indexed citations
10.
Hoebel, Katharina, Vincent Andrearczyk, Andrew Beers, et al.. (2020). An exploration of uncertainty information for segmentation quality assessment. ArODES (HES-SO (https://www.hes-so.ch/)). 55–55. 20 indexed citations
11.
Chang, Ken, Andrew Beers, Jay Patel, et al.. (2020). Multi-Institutional Assessment and Crowdsourcing Evaluation of Deep Learning for Automated Classification of Breast Density. Journal of the American College of Radiology. 17(12). 1653–1662. 37 indexed citations
12.
Li, Matthew, Ken Chang, Connie Y. Chang, et al.. (2020). Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging. npj Digital Medicine. 3(1). 48–48. 77 indexed citations
13.
Hoebel, Katharina, Jay Patel, Andrew Beers, et al.. (2020). Radiomics Repeatability Pitfalls in a Scan-Rescan MRI Study of Glioblastoma. Radiology Artificial Intelligence. 3(1). e190199–e190199. 39 indexed citations
14.
Silva, Michael A., Jay Patel, Vasileios K. Kavouridis, et al.. (2019). Machine Learning Models can Detect Aneurysm Rupture and Identify Clinical Features Associated with Rupture. World Neurosurgery. 131. e46–e51. 50 indexed citations
15.
Lo, William Chun Yip, Néstor Uribe‐Patarroyo, Katharina Hoebel, et al.. (2019). Balloon catheter-based radiofrequency ablation monitoring in porcine esophagus using optical coherence tomography. Biomedical Optics Express. 10(4). 2067–2067. 17 indexed citations
16.
Patel, Jay, Andrew Beers, James M. Brown, et al.. (2019). NIMG-43. LONGITUDINAL TRACKING AND GROWTH RATE CHARACTERIZATION OF BRAIN METASTASES ON MAGNETIC RESONANCE IMAGING. Neuro-Oncology. 21(Supplement_6). vi170–vi171.
17.
Parsons, Michael, Katharina Hoebel, Andrew Beers, et al.. (2018). NCOG-04. EFFECTS OF PROTON RADIATION ON BRAIN STRUCTURE AND FUNCTION IN LOW GRADE GLIOMA. Neuro-Oncology. 20(suppl_6). vi173–vi173. 2 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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