Karin Stacke
Impact in
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 10%
- AI in cancer detection
- Domain Adaptation and Few-Shot Learning
Papers in
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- AI in cancer detection 6
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- Radiomics and Machine Learning in Medical Imaging 3
- Co-authors
- Claes Lundström (4 shared papers)Gabriel Eilertsen (3 shared papers)Jonas Unger (3 shared papers)Martin Hedlund (1 shared paper)Jesper Molin (1 shared paper)Daniel Forsberg (1 shared paper)Darren Treanor (1 shared paper)Mischa Woisetschläger (1 shared paper)
- Journals
- Medical Physics (1 paper)Journal of Digital Imaging (1 paper)IEEE Journal of Biomedical and Health Informatics (1 paper)Linköping studies in science and technology. Dissertations (1 paper)KTH Publication Database DiVA (KTH Royal Institute of Technology) (1 paper)
- Partner nations
- SwedenUnited StatesUnited Kingdom
In The Last Decade
Karin Stacke
7 papers receiving 200 citations
Peers
Comparison fields: 5 of 54
- Health Informatics 12
- Artificial Intelligence 140
- Radiology, Nuclear Medicine and Imaging 71
- Biophysics 16
- Computer Vision and Pattern Recognition 56
Countries citing papers authored by Karin Stacke
This map shows the geographic impact of Karin Stacke'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 Karin Stacke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Karin Stacke more than expected).
Fields of papers citing papers by Karin Stacke
This network shows the impact of papers produced by Karin Stacke. 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 Karin Stacke. The network helps show where Karin Stacke may publish in the future.
Co-authors
The 14 scholars most cited alongside Karin Stacke, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 160 | |
| 2 | 2022 | 18 | |
| 3 | 2020 | 13 | |
| 4 | Evaluation of Contrastive Predictive Coding for Histopathology Applications | 2020 | 5 |
| 5 | 2022 | 2 | |
| 6 | 2024 | 2 | |
| 7 | 2021 | 2 |
About Karin Stacke
Karin Stacke is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine and Oncology, having authored 7 papers that have together received 202 indexed citations. Recurring topics across this work include AI in cancer detection (6 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Digital Imaging for Blood Diseases (2 papers), Artificial Intelligence in Healthcare and Education (1 paper), Renal cell carcinoma treatment (1 paper), Colorectal Cancer Screening and Detection (1 paper), Cell Image Analysis Techniques (1 paper) and Cervical Cancer and HPV Research (1 paper). The work is most often cited by research in Health Informatics (12 citations), Artificial Intelligence (140 citations), Radiology, Nuclear Medicine and Imaging (71 citations), Biophysics (16 citations) and Computer Vision and Pattern Recognition (56 citations). Karin Stacke has collaborated with scholars based in Sweden, United States and United Kingdom. Frequent co-authors include Claes Lundström, Gabriel Eilertsen, Jonas Unger, Martin Hedlund, Jesper Molin, Daniel Forsberg, Darren Treanor, Mischa Woisetschläger, Tie Liang and Justin R. Tse. Their work appears in journals such as Medical Physics, Journal of Digital Imaging, IEEE Journal of Biomedical and Health Informatics, Linköping studies in science and technology. Dissertations and KTH Publication Database DiVA (KTH Royal Institute of Technology).
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.