Anurag Vaidya

2.2k citations
10 papers · 865 indexed · 3 hit papers · h-index 8
Topics
AI in cancer detection (6 papers)Radiomics and Machine Learning in Medical Imaging (3 papers)Digital Imaging for Blood Diseases (2 papers)
Partner nations
United States

In The Last Decade

Anurag Vaidya

8 papers receiving 852 citations

Hit Papers

Artificial intelligence for multimodal data integration i...2022202620232024202220242023100200300

Peers

Anurag Vaidya
Comparison fields: 5 of 97
  • Artificial Intelligence 480
  • Radiology, Nuclear Medicine and Imaging 407
  • Molecular Biology 130
  • Health Informatics 130
  • Computer Vision and Pattern Recognition 117
Replace Chengkuan Chen with:
Chengkuan Chen United States
Mane Williams United States
Guillaume Jaume United States
Dyke Ferber Germany
Arash Mohtashamian United States
Thomas de Bel Netherlands
Andrew Zhang United States
Benoît Schmauch France
Pierre Courtiol France
Maha Shady United States
Anurag Vaidya relative to Chengkuan Chen United States Chengkuan Chen's profile →
Citations per field
00.5×1.5×2.4×
Chengkuan Chen · 1×
Citations per year

Countries citing papers authored by Anurag Vaidya

Since Specialization
Citations

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

Fields of papers citing papers by Anurag Vaidya

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Anurag Vaidya

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

All Works

10 of 10 papers shown
#WorkIndexed citations
1
Towards a general-purpose foundation model for computational pathologybreakdown →
331
2 10
3 32
4 39
5 0
6 0
7
Artificial intelligence for digital and computational pathologybreakdown →
103
8
Artificial intelligence for multimodal data integration in oncologybreakdown →
333
9 7
10 10

About Anurag Vaidya

Anurag Vaidya is a scholar working on Health Informatics, Biophysics and Artificial Intelligence, having authored 10 papers that have together received 865 indexed citations. Recurring topics across this work include AI in cancer detection (6 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Digital Imaging for Blood Diseases (2 papers). The work is most often cited by research in Health Informatics (130 citations), Radiology, Nuclear Medicine and Imaging (407 citations) and Artificial Intelligence (480 citations). Anurag Vaidya has collaborated with scholars based in United States. Frequent co-authors include Faisal Mahmood, Drew F. K. Williamson, Ming Y. Lu, Richard J. Chen, Muhammad Shaban, Bowen Chen, Guillaume Jaume, Andrew H. Song, Tiffany Chen and Jana Lipková. Their work appears in journals such as Nature Medicine, Cancer Cell and Neuro-Oncology.

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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