Neerav Karani
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging
- Biomedical Engineering
- Neurology
- Co-authors
- Ender KonukoğluKrishna ChaitanyaErtunç ErdilChristine TannerLin ZhangPhilipp FürnstahlOrçun GökselSebastian Kozerke
- Topics
- Radiomics and Machine Learning in Medical Imaging (3 papers)Medical Imaging and Analysis (2 papers)Medical Imaging Techniques and Applications (2 papers)
- Journals
- IEEE Transactions on Medical ImagingSAE technical papers on CD-ROM/SAE technical paper seriesMedical Image Analysis
- Partner nations
- SwitzerlandIndia
In The Last Decade
Neerav Karani
6 papers receiving 226 citations
Hit Papers
Peers
Comparison fields: 5 of 55
- Computer Vision and Pattern Recognition 144
- Artificial Intelligence 114
- Radiology, Nuclear Medicine and Imaging 102
- Biomedical Engineering 40
- Neurology 33
Countries citing papers authored by Neerav Karani
This map shows the geographic impact of Neerav Karani'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 Neerav Karani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Neerav Karani more than expected).
Fields of papers citing papers by Neerav Karani
This network shows the impact of papers produced by Neerav Karani. 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 Neerav Karani. The network helps show where Neerav Karani may publish in the future.
Co-authorship network of co-authors of Neerav Karani
This figure shows the co-authorship network connecting the top 25 collaborators of Neerav Karani. A scholar is included among the top collaborators of Neerav Karani 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 Neerav Karani. Neerav Karani is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentationbreakdown → | 112 |
| 2 | 103 | |
| 3 | 6 | |
| 4 | 3 | |
| 5 | 6 | |
| 6 | 1 |
About Neerav Karani
Neerav Karani is a scholar working on Radiation, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition, having authored 6 papers that have together received 231 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (3 papers), Medical Imaging and Analysis (2 papers) and Medical Imaging Techniques and Applications (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (144 citations), Radiology, Nuclear Medicine and Imaging (102 citations) and Neurology (33 citations). Neerav Karani has collaborated with scholars based in Switzerland and India. Frequent co-authors include Ender Konukoğlu, Krishna Chaitanya, Ertunç Erdil, Christine Tanner, Lin Zhang, Philipp Fürnstahl, Orçun Göksel and Sebastian Kozerke. Their work appears in journals such as IEEE Transactions on Medical Imaging, SAE technical papers on CD-ROM/SAE technical paper series and Medical Image Analysis.
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.