Urspeter Knecht

642 total citations
20 papers, 332 citations indexed

About

Urspeter Knecht is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics and Neurology. According to data from OpenAlex, Urspeter Knecht has authored 20 papers receiving a total of 332 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Radiology, Nuclear Medicine and Imaging, 14 papers in Genetics and 5 papers in Neurology. Recurrent topics in Urspeter Knecht's work include Glioma Diagnosis and Treatment (14 papers), Radiomics and Machine Learning in Medical Imaging (13 papers) and Brain Tumor Detection and Classification (5 papers). Urspeter Knecht is often cited by papers focused on Glioma Diagnosis and Treatment (14 papers), Radiomics and Machine Learning in Medical Imaging (13 papers) and Brain Tumor Detection and Classification (5 papers). Urspeter Knecht collaborates with scholars based in Switzerland, Austria and Czechia. Urspeter Knecht's co-authors include Roland Wiest, Mauricio Reyes, Raphael Meier, Johannes Slotboom, Philippe Schucht, Stefan Bauer, Richard McKinley, Ekkehard Hewer, Yannick Suter and Waldo Valenzuela and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Urspeter Knecht

19 papers receiving 330 citations

Peers

Urspeter Knecht

RNMI

GENET

NEURO

CVPR

Roelant S. Eijgelaar Netherlands

Jason Cheng United States

Patricia Svolos Greece

Domenique M. J. Müller Netherlands

Florian Kofler Germany

Marwa Ismail United States

Théo Estienne France

Enzo Battistella France

Reza Farjam United States

Paul A. Armitage United Kingdom

Roelant S. Eijgelaar Netherlands

Urspeter Knecht

190 ×0.8

RNMI

131 ×0.9

GENET

71 ×0.7

NEURO

37 ×0.5

CVPR

32 ×0.7

Citations per year, relative to Urspeter Knecht Urspeter Knecht (= 1×) peers Roelant S. Eijgelaar

Countries citing papers authored by Urspeter Knecht

Since Specialization

Citations

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

Fields of papers citing papers by Urspeter Knecht

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Urspeter Knecht

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

All Works

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20 of 20 papers shown

Suter, Yannick, Raphael Meier, Philippe Schucht, et al.. (2023). Evaluating automated longitudinal tumor measurements for glioblastoma response assessment. SHILAP Revista de lepidopterología. 3. 1211859–1211859. 2 indexed citations

Suter, Yannick, Urspeter Knecht, Waldo Valenzuela, et al.. (2022). The LUMIERE dataset: Longitudinal Glioblastoma MRI with expert RANO evaluation. Scientific Data. 9(1). 768–768. 13 indexed citations

Suter, Yannick, Urspeter Knecht, Waldo Valenzuela, et al.. (2020). Radiomics for glioblastoma survival analysis in pre-operative MRI: exploring feature robustness, class boundaries, and machine learning techniques. Cancer Imaging. 20(1). 55–55. 43 indexed citations

Meier, Raphael, Aurélie Pahud de Mortanges, Roland Wiest, & Urspeter Knecht. (2020). Exploratory Analysis of Qualitative MR Imaging Features for the Differentiation of Glioblastoma and Brain Metastases. Frontiers in Oncology. 10. 581037–581037. 12 indexed citations

Ermiş, Ekin, Alain Jungo, Raphael Meier, et al.. (2020). Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning. Radiation Oncology. 15(1). 100–100. 51 indexed citations

Hewer, Ekkehard, Yara Banz, Urspeter Knecht, Matthias S. Dettmer, & Erik Vassella. (2020). Seminal vesicle carcinoma presenting with malignant ascites. Diagnostic Cytopathology. 48(8). 785–786.

Rebsamen, Michael, Urspeter Knecht, Mauricio Reyes, et al.. (2019). Divide and Conquer: Stratifying Training Data by Tumor Grade Improves Deep Learning-Based Brain Tumor Segmentation. Frontiers in Neuroscience. 13. 1182–1182. 15 indexed citations

Meier, Raphael, Urspeter Knecht, Mauricio Reyes, et al.. (2019). Analysis of metabolic abnormalities in high‐grade glioma using MRSI and convex NMF. NMR in Biomedicine. 32(8). e4109–e4109. 5 indexed citations

Herrmann, Evelyn, Ekin Ermiş, Raphael Meier, et al.. (2018). Fully Automated Segmentation of the Brain Resection Cavity for Radiation Target Volume Definition in Glioblastoma Patients. International Journal of Radiation Oncology*Biology*Physics. 102(3). S194–S194. 3 indexed citations

10.

Porz, Nicole, Urspeter Knecht, Beate Sick, et al.. (2018). Computer-aided radiological diagnostics improves the preoperative diagnoses of medulloblastoma, pilocytic astrocytoma, and ependymoma. 2(2). 2514183X1878660–2514183X1878660. 2 indexed citations

11.

Herrmann, Evelyn, Ekin Ermiş, Alain Jungo, et al.. (2018). P01.088 Brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning. Neuro-Oncology. 20(suppl_3). iii250–iii251. 3 indexed citations

12.

Meier, Raphael, Urspeter Knecht, Evelyn Herrmann, et al.. (2018). On the relation between MR spectroscopy features and the distance to MRI‐visible solid tumor in GBM patients. Magnetic Resonance in Medicine. 80(6). 2339–2355. 5 indexed citations

13.

Meier, Raphael, Nicole Porz, Urspeter Knecht, et al.. (2017). Automatic estimation of extent of resection and residual tumor volume of patients with glioblastoma. Journal of neurosurgery. 127(4). 798–806. 30 indexed citations

14.

Ortega‐Martorell, Sandra, Johannes Slotboom, Urspeter Knecht, et al.. (2016). A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases. RECERCAT (Consorci de Serveis Universitaris de Catalunya). 247–252. 4 indexed citations

15.

Porz, Nicole, Raphael Meier, Rajeev Verma, et al.. (2016). Fully Automated Enhanced Tumor Compartmentalization: Man vs. Machine Reloaded. PLoS ONE. 11(11). e0165302–e0165302. 22 indexed citations

16.

Meier, Raphael, Urspeter Knecht, Stefan Bauer, et al.. (2016). Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry. Scientific Reports. 6(1). 23376–23376. 79 indexed citations

17.

Fiechter, Michael, Ekkehard Hewer, Urspeter Knecht, et al.. (2016). Adult anaplastic pilocytic astrocytoma – a diagnostic challenge? A case series and literature review. Clinical Neurology and Neurosurgery. 147. 98–104. 7 indexed citations

18.

McKinley, Richard, et al.. (2016). Automatic quality control in clinical ¹H MRSI of brain cancer. NMR in Biomedicine. 29(5). 563–575. 25 indexed citations

19.

Pica, Alessia, et al.. (2014). P16.03 * UNIFYING CLINICAL ROUTINE BRAIN TUMOR MR-SPECTROSCOPY AND MR-IMAGE ANALYSIS: NOVEL JMRUI PLUG-INS FOR BRAIN TUMOR ANALYSIS. Neuro-Oncology. 16(suppl 2). ii78–ii78. 2 indexed citations

20.

Zimmerli, Stefan, Urspeter Knecht, & Stephen L. Leib. (2007). A model of cerebral aspergillosis in non-immunosuppressed nursing rats. Acta Neuropathologica. 114(4). 411–418. 9 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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