Federico Pineda

675 citations
26 papers · 503 indexed · h-index 13
Topics
MRI in cancer diagnosis (24 papers)Advanced MRI Techniques and Applications (22 papers)Radiomics and Machine Learning in Medical Imaging (8 papers)
Partner nations
United StatesJapanChina

In The Last Decade

Federico Pineda

24 papers receiving 500 citations

Peers

Federico Pineda
Comparison fields: 5 of 59
  • Radiology, Nuclear Medicine and Imaging 403
  • Pulmonary and Respiratory Medicine 59
  • Biomedical Engineering 50
  • Cancer Research 47
  • Pathology and Forensic Medicine 46
Replace Jason B. White with:
Jason B. White United States
Jianye Liang China
Kjersti V. Lund Norway
Mei-Lin W. Ah-See United Kingdom
Jayant Narang United States
Edith Lorenz Austria
Joseph Sia Australia
Sonia P. Li United Kingdom
Julia Skliarenko Canada
Sarah C. Brüningk Switzerland
Federico Pineda relative to Jason B. White United States Jason B. White's profile →
Citations per field
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Citations per year

Countries citing papers authored by Federico Pineda

Since Specialization
Citations

This map shows the geographic impact of Federico Pineda'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 Federico Pineda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Federico Pineda more than expected).

Fields of papers citing papers by Federico Pineda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Federico Pineda. 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 Federico Pineda. The network helps show where Federico Pineda may publish in the future.

Co-authorship network of co-authors of Federico Pineda

This figure shows the co-authorship network connecting the top 25 collaborators of Federico Pineda. A scholar is included among the top collaborators of Federico Pineda 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 Federico Pineda. Federico Pineda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 4
3 1
4 6
5 5
6 6
7 24
8 3
9 4
10 13
11 46
12 37
13 2
14 6
15 32
16 67
17 54
18 17
19 13
20 13

About Federico Pineda

Federico Pineda is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomaterials and Polymers and Plastics, having authored 26 papers that have together received 503 indexed citations. Recurring topics across this work include MRI in cancer diagnosis (24 papers), Advanced MRI Techniques and Applications (22 papers) and Radiomics and Machine Learning in Medical Imaging (8 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (403 citations), Modeling and Simulation (16 citations) and Biomaterials (41 citations). Federico Pineda has collaborated with scholars based in United States, Japan and China. Frequent co-authors include Gregory S. Karczmar, Hiroyuki Abé, David Schacht, Xiaobing Fan, Milica Medved, Naoko Mori, Keiko Tsuchiya, Yulei Jiang, Thomas E. Yankeelov and Shiyang Wang. Their work appears in journals such as PLoS ONE, Cancer Research and Magnetic Resonance in Medicine.

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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