Daniela Raicu

84 papers and 590 indexed citations i.

About

Daniela Raicu is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Daniela Raicu has authored 84 papers receiving a total of 590 indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Artificial Intelligence, 40 papers in Radiology, Nuclear Medicine and Imaging and 29 papers in Computer Vision and Pattern Recognition. Recurrent topics in Daniela Raicu’s work include Radiomics and Machine Learning in Medical Imaging (31 papers), AI in cancer detection (27 papers) and Image Retrieval and Classification Techniques (17 papers). Daniela Raicu is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (31 papers), AI in cancer detection (27 papers) and Image Retrieval and Classification Techniques (17 papers). Daniela Raicu collaborates with scholars based in United States, Mexico and Australia. Daniela Raicu's co-authors include Jacob Furst, David S. Channin, Samuel G. Armato, Katherine J. Strandburg, Noriko Tomuro, Yu Zhang, Jonathan Gemmell, Samah Fodeh, Ana M. León and Michael Lam and has published in prestigious journals such as Bioinformatics, PLoS ONE and BMC Bioinformatics.

In The Last Decade

Co-authorship network of co-authors of Daniela Raicu i

Fields of papers citing papers by Daniela Raicu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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.

Countries citing papers authored by Daniela Raicu

Since Specialization
Citations

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).

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.

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2025