Daniel Racoceanu
- Artificial Intelligence top 0.5%
- Computer Vision and Pattern Recognition top 0.5%
- Radiology, Nuclear Medicine and Imaging top 1%
- Biophysics top 0.5%
- Molecular Biology
- Co-authors
- Humayun IrshadLudovic RouxAntoine VeillardNoureddine ZerhouniRyad ZemouriMonjoy SahaNicolas LoménieChandan Chakraborty
- Topics
- AI in cancer detection (38 papers)Cell Image Analysis Techniques (23 papers)Image Retrieval and Classification Techniques (16 papers)
- Journals
- Journal of Clinical OncologySHILAP Revista de lepidopterologíaAmerican Journal Of Pathology
In The Last Decade
Daniel Racoceanu
90 papers receiving 2.7k citations
Hit Papers
Peers
Comparison fields: 5 of 162
- Artificial Intelligence 1.8k
- Computer Vision and Pattern Recognition 1.3k
- Radiology, Nuclear Medicine and Imaging 1.0k
- Biophysics 518
- Molecular Biology 235
Countries citing papers authored by Daniel Racoceanu
This map shows the geographic impact of Daniel Racoceanu'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 Daniel Racoceanu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Racoceanu more than expected).
Fields of papers citing papers by Daniel Racoceanu
This network shows the impact of papers produced by Daniel Racoceanu. 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 Daniel Racoceanu. The network helps show where Daniel Racoceanu may publish in the future.
Co-authorship network of co-authors of Daniel Racoceanu
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Racoceanu. A scholar is included among the top collaborators of Daniel Racoceanu 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 Daniel Racoceanu. Daniel Racoceanu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 9 | |
| 5 | 0 | |
| 6 | 16 | |
| 7 | 69 | |
| 8 | 7 | |
| 9 | 127 | |
| 10 | 44 | |
| 11 | 147 | |
| 12 | 4 | |
| 13 | Gland segmentation in colon histology images: The glas challenge contestbreakdown → | 570 |
| 14 | 5 | |
| 15 | 8 | |
| 16 | 83 | |
| 17 | Nuclear pleomorphism scoring by selective cell nuclei detection | 35 |
| 18 | 0 | |
| 19 | 1 | |
| 20 | A singular perturbation approach to modeling and resolution of Markov chains | 1 |
About Daniel Racoceanu
Daniel Racoceanu is a scholar working on Biophysics, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 97 papers that have together received 2.8k indexed citations. Recurring topics across this work include AI in cancer detection (38 papers), Cell Image Analysis Techniques (23 papers) and Image Retrieval and Classification Techniques (16 papers). The work is most often cited by research in Biophysics (518 citations), Computer Vision and Pattern Recognition (1.3k citations) and Artificial Intelligence (1.8k citations). Daniel Racoceanu has collaborated with scholars based in France, Singapore and Peru. Frequent co-authors include Humayun Irshad, Ludovic Roux, Antoine Veillard, Noureddine Zerhouni, Ryad Zemouri, Monjoy Saha, Nicolas Loménie, Chandan Chakraborty, Bassem Ben Cheikh and Purang Abolmaesumi. Their work appears in journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and American Journal Of Pathology.
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.