Divya Khullar

977 citations
16 papers · 839 · 1 hit paper · h-index 8

Impact in

Papers in

Divya Khullar

16 papers receiving 829 citations

Divya Khullar's Hit Papers

Exploring feature-based approaches in PET images for predicting cancer treatment outcomes 2008 · 413 citations
4130+6+12Years since publication100200300400

Peers

Divya Khullar
Comparison fields: 5 of 34
  • Radiation 289
  • Radiology, Nuclear Medicine and Imaging 451
  • Computer Vision and Pattern Recognition 116
  • Pulmonary and Respiratory Medicine 57
  • Otorhinolaryngology 10
Replace Alev K. Erdi with:
Alev K. Erdi United States
Greetje Groenendaal Netherlands
Andrea Schaefer-Schuler Germany
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Montserrat Carles Germany
Enrico Menghi Italy
Walter H. Choi United States
Akihiro Takemura Japan
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Citations per field
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Citations per year

Countries citing papers authored by Divya Khullar

Since Specialization
Citations

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

Fields of papers citing papers by Divya Khullar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Divya Khullar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Divya Khullar Line = papers co-authored together Divya Khullar links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1
Exploring feature-based approaches in PET images for predicting cancer treatment outcomes
Hit paper breakdown →
2008413
2 2006100
3 2007100
4 200789
5 200942
6 200831
7 200928
8 200519
9 20054
10 20063
11 20083
12 20052
13 20072
14 20061
15 20071
16 20081

About Divya Khullar

Divya Khullar is a scholar working on Radiation, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Nuclear Energy and Engineering, having authored 16 papers that have together received 839 indexed citations. Recurring topics across this work include Advanced Radiotherapy Techniques (11 papers), Medical Image Segmentation Techniques (4 papers), Cardiac Imaging and Diagnostics (2 papers), Lung Cancer Diagnosis and Treatment (1 paper), Image and Signal Denoising Methods (1 paper), Radiation Dose and Imaging (1 paper), Effects of Radiation Exposure (1 paper) and Radiomics and Machine Learning in Medical Imaging (1 paper). The work is most often cited by research in Radiation (289 citations), Radiology, Nuclear Medicine and Imaging (451 citations), Computer Vision and Pattern Recognition (116 citations), Pulmonary and Respiratory Medicine (57 citations) and Otorhinolaryngology (10 citations). Divya Khullar has collaborated with scholars based in United States, Israel and Italy. Frequent co-authors include Issam El Naqa, Joseph O. Deasy, Perry W. Grigsby, Deshan Yang, Aditya Apte, Summer R. Chaudhari, Wade L. Thorstad, Eric Donald Donnelly, Elizabeth A. Kidd and Richard A. Laforest. Their work appears in journals such as Medical Physics, International Journal of Radiation Oncology*Biology*Physics, Radiotherapy and Oncology, Pattern Recognition and IEEE Symposium Conference Record Nuclear Science 2004..

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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