Neerav Karani

925 total citations · 1 hit paper
6 papers, 231 citations indexed

About

Neerav Karani is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Radiation. According to data from OpenAlex, Neerav Karani has authored 6 papers receiving a total of 231 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Radiology, Nuclear Medicine and Imaging, 2 papers in Computer Vision and Pattern Recognition and 2 papers in Radiation. Recurrent topics in Neerav Karani's 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). Neerav Karani is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (3 papers), Medical Imaging and Analysis (2 papers) and Medical Imaging Techniques and Applications (2 papers). Neerav Karani collaborates with scholars based in Switzerland and India. Neerav Karani's co-authors include Ender Konukoğlu, Krishna Chaitanya, Ertunç Erdil, Christine Tanner, Orçun Göksel, Philipp Fürnstahl, Lin Zhang and Sebastian Kozerke and has published in prestigious journals such as IEEE Transactions on Medical Imaging, SAE technical papers on CD-ROM/SAE technical paper series and Medical Image Analysis.

In The Last Decade

Neerav Karani

6 papers receiving 226 citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation

2023 112 citations Krishna Chaitanya, Ertunç Erdil et al. Medical Image Analysis profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Neerav Karani Switzerland	4	144	114	102	40	33	6	231
Constantin Seibold Germany	7	128 0.9×	127 1.1×	86 0.8×	40 1.0×	28 0.8×	22	258
Abin Jose Germany	7	113 0.8×	83 0.7×	93 0.9×	35 0.9×	44 1.3×	22	278
Amirali Molaei Iran	3	90 0.6×	72 0.6×	99 1.0×	44 1.1×	46 1.4×	5	238
Minfeng Xu China	8	183 1.3×	103 0.9×	114 1.1×	51 1.3×	56 1.7×	15	288
Changfa Shi China	8	157 1.1×	93 0.8×	145 1.4×	56 1.4×	45 1.4×	15	298
Junde Wu China	8	120 0.8×	89 0.8×	110 1.1×	30 0.8×	32 1.0×	14	274
Zhe Xu China	8	176 1.2×	162 1.4×	115 1.1×	46 1.1×	50 1.5×	27	314
Thomas M. Deserno Germany	8	97 0.7×	50 0.4×	66 0.6×	30 0.8×	23 0.7×	12	226
Yunlu Yan China	4	82 0.6×	60 0.5×	114 1.1×	51 1.3×	25 0.8×	6	213
Xingle An United States	2	113 0.8×	96 0.8×	149 1.5×	49 1.2×	25 0.8×	3	229

Countries citing papers authored by Neerav Karani

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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6 of 6 papers shown

1.

Chaitanya, Krishna, Ertunç Erdil, Neerav Karani, & Ender Konukoğlu. (2023). Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation. Medical Image Analysis. 87. 102792–102792. 112 indexed citations breakdown →

2.

Karani, Neerav, Ertunç Erdil, Krishna Chaitanya, & Ender Konukoğlu. (2020). Test-time adaptable neural networks for robust medical image segmentation. Medical Image Analysis. 68. 101907–101907. 103 indexed citations

3.

Karani, Neerav, Lin Zhang, Christine Tanner, & Ender Konukoğlu. (2019). An image interpolation approach for acquisition time reduction in navigator-based 4D MRI. Medical Image Analysis. 54. 20–29. 6 indexed citations

4.

Karani, Neerav, Christine Tanner, Sebastian Kozerke, & Ender Konukoğlu. (2018). Reducing Navigators in Free-Breathing Abdominal MRI via Temporal Interpolation Using Convolutional Neural Networks. IEEE Transactions on Medical Imaging. 37(10). 2333–2343. 3 indexed citations

5.

Karani, Neerav, et al.. (2017). Interactive segmentation in MRI for orthopedic surgery planning: bone tissue. International Journal of Computer Assisted Radiology and Surgery. 12(6). 1031–1039. 6 indexed citations

6.

Karani, Neerav, et al.. (2015). FE Driven Pedestrian Friendly Front End Design and Its Correlation with Physical Test. SAE technical papers on CD-ROM/SAE technical paper series. 1 indexed citations

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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