Tassilo Wald
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
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- Radiomics and Machine Learning in Medical Imaging 5
- COVID-19 diagnosis using AI 4
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- Advanced Neural Network Applications 4
- Medical Image Segmentation Techniques 2
- Visual Attention and Saliency Detection 1
- Co-authors
- Klaus Maier‐Hein (16 shared papers)Fabian Isensee (9 shared papers)Constantin Ulrich (8 shared papers)Michael Baumgartner (6 shared papers)Paul F. Jäger (6 shared papers)Saikat Roy (4 shared papers)Maximilian Zenk (6 shared papers)Oyunbileg von Stackelberg (3 shared papers)
- Journals
- Insights into Imaging (1 paper)European Radiology (1 paper)Frontiers in Medicine (1 paper)European Radiology Experimental (1 paper)Lecture notes in computer science (7 papers)
- Partner nations
- GermanyUnited StatesLatvia
In The Last Decade
Tassilo Wald
11 papers receiving 197 citations
Tassilo Wald's Hit Papers
Peers
Comparison fields: 5 of 54
- Radiology, Nuclear Medicine and Imaging 90
- Health Informatics 4
- Neurology 20
- Computer Vision and Pattern Recognition 55
- Structural Biology 3
Countries citing papers authored by Tassilo Wald
This map shows the geographic impact of Tassilo Wald'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 Tassilo Wald with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tassilo Wald more than expected).
Fields of papers citing papers by Tassilo Wald
This network shows the impact of papers produced by Tassilo Wald. 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 Tassilo Wald. The network helps show where Tassilo Wald may publish in the future.
Co-authors
The 25 scholars most cited alongside Tassilo Wald, 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 | nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation Hit paper breakdown → | 2024 | 116 |
| 2 | 2023 | 23 | |
| 3 | 2023 | 22 | |
| 4 | 2024 | 15 | |
| 5 | 2023 | 14 | |
| 6 | 2023 | 5 | |
| 7 | 2024 | 5 | |
| 8 | 2024 | 5 | |
| 9 | 2025 | 3 | |
| 10 | 2025 | 1 | |
| 11 | 2024 | 1 | |
| 12 | 2024 | 0 | |
| 13 | 2025 | 0 | |
| 14 | 2024 | 0 | |
| 15 | 2025 | 0 | |
| 16 | 2024 | 0 | |
| 17 | 2025 | 0 |
About Tassilo Wald
Tassilo Wald is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Artificial Intelligence, Pulmonary and Respiratory Medicine and Biomedical Engineering, having authored 17 papers that have together received 210 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (5 papers), COVID-19 diagnosis using AI (4 papers), Advanced Neural Network Applications (4 papers), AI in cancer detection (3 papers), Medical Image Segmentation Techniques (2 papers), Chronic Obstructive Pulmonary Disease (COPD) Research (2 papers), Lung Cancer Diagnosis and Treatment (2 papers) and Visual Attention and Saliency Detection (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (90 citations), Health Informatics (4 citations), Neurology (20 citations), Computer Vision and Pattern Recognition (55 citations) and Structural Biology (3 citations). Tassilo Wald has collaborated with scholars based in Germany, United States and Latvia. Frequent co-authors include Klaus Maier‐Hein, Fabian Isensee, Constantin Ulrich, Michael Baumgartner, Paul F. Jäger, Saikat Roy, Maximilian Zenk, Oyunbileg von Stackelberg, Vivienn Weru and Oliver Weinheimer. Their work appears in journals such as Insights into Imaging, European Radiology, Frontiers in Medicine, European Radiology Experimental and Lecture notes in computer science.
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.