ResearcherTechnical University of MunichActive in the last 2 years
Friederike Jungmann
Matched on ORCID iD 0000-0002-9600-4094; OpenAlex author ID A5028264043. Sources: OpenAlex.
Their work
- Papers
- 6 (first author 2, last author 0)
- Trials
- 0
- Grants
- 0
- Labs led
- 0
- Collaborators
- 20
2020: 3 papers · 2021: 2 papers · 2022: none · 2023: none · 2024: 1 paper
Papers 6
- Prognostic value of deep learning-derived body composition in advanced pancreatic cancer-a retrospective multicenter study. Co-author DOI 10.1016/j.esmoop.2023.102219↗
- [18F]FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma. First author DOI 10.1186/s13550-021-00808-4↗
- Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging. Co-author DOI 10.3390/cancers13092069↗
- Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma. Co-author DOI 10.3390/jcm9030724↗
- Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic and Image-Derived Parameters. First author DOI 10.3390/jcm9051250↗
- Deep Convolutional Neural Network-Assisted Feature Extraction for Diagnostic Discrimination and Feature Visualization in Pancreatic Ductal Adenocarcinoma (PDAC… Co-author DOI 10.3390/jcm9124013↗
Closest collaborators
The 20 strongest of 20 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.
- Georgios A. Kaissis · 6 shared papers, 2020–2024 · strength 0.68
- Paper Prognostic value of deep learning-derived body composition in advanced pancreatic cancer-a retrospective multicenter st… DOI 10.1016/j.esmoop.2023.102219↗
- Paper Deep Convolutional Neural Network-Assisted Feature Extraction for Diagnostic Discrimination and Feature Visualization i… DOI 10.3390/jcm9124013↗
- Paper Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging. DOI 10.3390/cancers13092069↗
- Rickmer Braren · 6 shared papers, 2020–2024 · strength 0.68
- Paper Prognostic value of deep learning-derived body composition in advanced pancreatic cancer-a retrospective multicenter st… DOI 10.1016/j.esmoop.2023.102219↗
- Paper Deep Convolutional Neural Network-Assisted Feature Extraction for Diagnostic Discrimination and Feature Visualization i… DOI 10.3390/jcm9124013↗
- Paper Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging. DOI 10.3390/cancers13092069↗
- Sebastian Ziegelmayer · 5 shared papers, 2020–2021 · strength 0.53
- Paper Deep Convolutional Neural Network-Assisted Feature Extraction for Diagnostic Discrimination and Feature Visualization i… DOI 10.3390/jcm9124013↗
- Paper Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging. DOI 10.3390/cancers13092069↗
- Paper [18F]FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma. DOI 10.1186/s13550-021-00808-4↗
- Felix N. Harder · 5 shared papers, 2020–2021 · strength 0.53
- Paper Deep Convolutional Neural Network-Assisted Feature Extraction for Diagnostic Discrimination and Feature Visualization i… DOI 10.3390/jcm9124013↗
- Paper Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging. DOI 10.3390/cancers13092069↗
- Paper [18F]FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma. DOI 10.1186/s13550-021-00808-4↗
- Wilko Weichert · 4 shared papers, 2020–2021 · strength 0.41
- Paper Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging. DOI 10.3390/cancers13092069↗
- Paper [18F]FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma. DOI 10.1186/s13550-021-00808-4↗
- Paper Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic a… DOI 10.3390/jcm9051250↗
- Fabian Karl Lohöfer · 4 shared papers, 2020–2021 · strength 0.41
- Paper Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging. DOI 10.3390/cancers13092069↗
- Paper [18F]FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma. DOI 10.1186/s13550-021-00808-4↗
- Paper Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic a… DOI 10.3390/jcm9051250↗
- Jens Thomas Siveke · 3 shared papers, 2020–2024 · strength 0.35
- Paper Prognostic value of deep learning-derived body composition in advanced pancreatic cancer-a retrospective multicenter st… DOI 10.1016/j.esmoop.2023.102219↗
- Paper [18F]FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma. DOI 10.1186/s13550-021-00808-4↗
- Paper Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma. DOI 10.3390/jcm9030724↗
- Alexander Muckenhuber · 3 shared papers, 2020–2021 · strength 0.29
- Paper [18F]FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma. DOI 10.1186/s13550-021-00808-4↗
- Paper Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic a… DOI 10.3390/jcm9051250↗
- Paper Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma. DOI 10.3390/jcm9030724↗
- Roland Michael Schmid · 3 shared papers, 2020–2021 · strength 0.29
- Paper [18F]FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma. DOI 10.1186/s13550-021-00808-4↗
- Paper Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic a… DOI 10.3390/jcm9051250↗
- Paper Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma. DOI 10.3390/jcm9030724↗
- Helmut Friess · 2 shared papers, 2020–2021 · strength 0.21
- Helmut Frieß · 2 shared papers, 2020–2021 · strength 0.20
- Hideo Andreas Baba · 1 shared paper, 2024 · strength 0.15
- Julius Keyl · 1 shared paper, 2024 · strength 0.15
- Stefan Kasper · 1 shared paper, 2024 · strength 0.15
- Timm M. Reißig · 1 shared paper, 2024 · strength 0.15
- Jens Kleesiek · 1 shared paper, 2024 · strength 0.15
- Maximilian Reichert · 1 shared paper, 2021 · strength 0.10
- Michael Quante · 1 shared paper, 2021 · strength 0.10
- İhsan Ekin Demir · 1 shared paper, 2021 · strength 0.10
- Anna Melissa Schlitter · 1 shared paper, 2020 · strength 0.10
How to reach this person
A chain of introductions through people who have published, run trials or held grants together (warm_path@1: fewest links first, then the strongest). Built from public records only; no contact details are kept.