Daniela Raicu
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
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- Image Retrieval and Classification Techniques
- Medical Image Segmentation Techniques
- Advanced Image and Video Retrieval Techniques
Papers in
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- AI in cancer detection 34
- Topic Modeling 11
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- Radiomics and Machine Learning in Medical Imaging 38
- COVID-19 diagnosis using AI 11
- Co-authors
- Jacob Furst (103 shared papers)Dong-Hui Xu (3 shared papers)David S. Channin (9 shared papers)Samuel G. Armato (11 shared papers)Noriko Tomuro (3 shared papers)Yu Zhang (3 shared papers)Katherine J. Strandburg (2 shared papers)Jonathan Gemmell (7 shared papers)
- Journals
- Journal of Digital Imaging (5 papers)Surgery (2 papers)Bioinformatics (1 paper)BMC Neuroscience (1 paper)Frontiers in Big Data (1 paper)
- Partner nations
- United StatesMexicoAustralia
In The Last Decade
Daniela Raicu
107 papers receiving 1.0k citations
Peers
Comparison fields: 5 of 116
- Radiology, Nuclear Medicine and Imaging 470
- Computer Vision and Pattern Recognition 343
- Artificial Intelligence 444
- Health Informatics 16
- Aging 18
Countries citing papers authored by Daniela Raicu
This map shows the geographic impact of Daniela Raicu'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 Daniela Raicu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniela Raicu more than expected).
Fields of papers citing papers by Daniela Raicu
This network shows the impact of papers produced by Daniela Raicu. 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 Daniela Raicu. The network helps show where Daniela Raicu may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniela Raicu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 120 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2004 | 116 | |
| 2 | CO-OCCURRENCE MATRICES FOR VOLUMETRIC DATA | 2004 | 84 |
| 3 | 2011 | 41 | |
| 4 | 2009 | 39 | |
| 5 | 2007 | 35 | |
| 6 | 2007 | 31 | |
| 7 | 2019 | 30 | |
| 8 | 2018 | 26 | |
| 9 | 2007 | 26 | |
| 10 | 2009 | 25 | |
| 11 | 2010 | 24 | |
| 12 | 2011 | 23 | |
| 13 | 2005 | 20 | |
| 14 | 2007 | 20 | |
| 15 | 2009 | 19 | |
| 16 | 2015 | 17 | |
| 17 | 2008 | 16 | |
| 18 | 2015 | 14 | |
| 19 | 2010 | 14 | |
| 20 | 2010 | 14 |
About Daniela Raicu
Daniela Raicu is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine and Molecular Biology, having authored 120 papers that have together received 1.1k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (38 papers), AI in cancer detection (34 papers), Lung Cancer Diagnosis and Treatment (23 papers), Image Retrieval and Classification Techniques (21 papers), Medical Image Segmentation Techniques (18 papers), Biomedical Text Mining and Ontologies (13 papers), COVID-19 diagnosis using AI (11 papers) and Topic Modeling (11 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (470 citations), Computer Vision and Pattern Recognition (343 citations), Artificial Intelligence (444 citations), Health Informatics (16 citations) and Aging (18 citations). Daniela Raicu has collaborated with scholars based in United States, Mexico and Australia. Frequent co-authors include Jacob Furst, Dong-Hui Xu, David S. Channin, Samuel G. Armato, Noriko Tomuro, Yu Zhang, Katherine J. Strandburg, Jonathan Gemmell, Alexander Rasin and Samah Fodeh. Their work appears in journals such as Journal of Digital Imaging, Surgery, Bioinformatics, BMC Neuroscience and Frontiers in Big Data.
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.