David Tellez
- Artificial Intelligence top 2%
- Radiology, Nuclear Medicine and Imaging top 5%
- Computer Vision and Pattern Recognition top 5%
- Oncology
- Biophysics top 5%
- Co-authors
- Jeroen van der LaakFrancesco CiompiGeert LitjensPéter BándiJohn‐Melle BokhorstWouter BultenMaschenka BalkenholWillem Vreuls
- Topics
- AI in cancer detection (7 papers)Radiomics and Machine Learning in Medical Imaging (4 papers)Breast Cancer Treatment Studies (2 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceMedical Image AnalysisLaboratory Investigation
- Partner nations
- NetherlandsSwedenBelgium
In The Last Decade
David Tellez
9 papers receiving 580 citations
Hit Papers
Peers
Comparison fields: 5 of 74
- Artificial Intelligence 480
- Radiology, Nuclear Medicine and Imaging 310
- Computer Vision and Pattern Recognition 243
- Oncology 95
- Biophysics 84
Countries citing papers authored by David Tellez
This map shows the geographic impact of David Tellez'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 David Tellez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Tellez more than expected).
Fields of papers citing papers by David Tellez
This network shows the impact of papers produced by David Tellez. 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 David Tellez. The network helps show where David Tellez may publish in the future.
Co-authorship network of co-authors of David Tellez
This figure shows the co-authorship network connecting the top 25 collaborators of David Tellez. A scholar is included among the top collaborators of David Tellez 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 David Tellez. David Tellez is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 6 | |
| 3 | 73 | |
| 4 | 17 | |
| 5 | Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathologybreakdown → | 317 |
| 6 | 146 | |
| 7 | 16 | |
| 8 | Gigapixel Whole-Slide Image Classification Using Unsupervised Image Compression And Contrastive Training | 5 |
| 9 | 7 |
About David Tellez
David Tellez is a scholar working on Biophysics, Media Technology and Artificial Intelligence, having authored 9 papers that have together received 592 indexed citations. Recurring topics across this work include AI in cancer detection (7 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Breast Cancer Treatment Studies (2 papers). The work is most often cited by research in Health Informatics (26 citations), Artificial Intelligence (480 citations) and Biophysics (84 citations). David Tellez has collaborated with scholars based in Netherlands, Sweden and Belgium. Frequent co-authors include Jeroen van der Laak, Francesco Ciompi, Geert Litjens, Péter Bándi, John‐Melle Bokhorst, Wouter Bulten, Maschenka Balkenhol, Willem Vreuls, P C Clahsen and Peter Bult. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Medical Image Analysis and Laboratory Investigation.
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.