Harini Veeraraghavan

7.3k total citations · 3 hit papers
100 papers, 4.6k citations indexed

About

Harini Veeraraghavan is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Harini Veeraraghavan has authored 100 papers receiving a total of 4.6k indexed citations (citations by other indexed papers that have themselves been cited), including 65 papers in Radiology, Nuclear Medicine and Imaging, 25 papers in Computer Vision and Pattern Recognition and 23 papers in Artificial Intelligence. Recurrent topics in Harini Veeraraghavan's work include Radiomics and Machine Learning in Medical Imaging (57 papers), Advanced Radiotherapy Techniques (21 papers) and Medical Imaging Techniques and Applications (16 papers). Harini Veeraraghavan is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (57 papers), Advanced Radiotherapy Techniques (21 papers) and Medical Imaging Techniques and Applications (16 papers). Harini Veeraraghavan collaborates with scholars based in United States, United Kingdom and France. Harini Veeraraghavan's co-authors include Joseph O. Deasy, Evis Sala, Nikos Papanikolopoulos, Jue Jiang, Hedvig Hricak, H. Alberto Vargas, Hebert Alberto Vargas, Duc Fehr, Alexandra Snyder and Andreas G. Wibmer and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Journal of Clinical Oncology.

In The Last Decade

Harini Veeraraghavan

97 papers receiving 4.5k citations

Hit Papers

align trajectories

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.

Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores

2015 321 citations Andreas G. Wibmer, Hedvig Hricak et al. European Radiology profile →
Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images

2015 304 citations Duc Fehr, Harini Veeraraghavan et al. profile →
Vision 20/20: Perspectives on automated image segmentation for radiotherapy

2014 284 citations G Sharp, Karl Fritscher et al. Medical Physics profile →

Peers

Harini Veeraraghavan

RNMI

PRM

ONCOL

Ruijiang Li United States

Mathieu Hatt France

Justin Kirby United States

John Freymann United States

Seán Walsh United States

Arthur Jochems Netherlands

Stephen Moore United States

Timo M. Deist Netherlands

Bruce A. Vendt United States

Rivka R. Colen United States

Ruijiang Li United States

Harini Veeraraghavan

3.1k ×1.0

RNMI

1.7k ×1.5

PRM

943 ×1.1

ONCOL

720 ×0.9

931 ×1.2

Citations per year, relative to Harini Veeraraghavan Harini Veeraraghavan (= 1×) peers Ruijiang Li

Countries citing papers authored by Harini Veeraraghavan

Since Specialization

Citations

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

Fields of papers citing papers by Harini Veeraraghavan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Harini Veeraraghavan

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

All Works

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20 of 20 papers shown

Thor, Maria, Vonetta M. Williams, Laura Cerviño, et al.. (2025). Under-representation for Female Pelvis Cancers in Commercial Auto-segmentation Solutions and Open-source Imaging Datasets. Clinical Oncology. 38. 103651–103651.

Chen, Monica F., Jennifer Ma, Mark G. Kris, et al.. (2025). Outcomes After Radiation for Oligoprogressive Disease Sites in Patients With EGFR -Mutant Lung Cancer Treated With Osimertinib. JCO Precision Oncology. 9(9). e2500047–e2500047. 1 indexed citations

Jiang, Jue, et al.. (2024). Artificial intelligence-based automated segmentation and radiotherapy dose mapping for thoracic normal tissues. Physics and Imaging in Radiation Oncology. 29. 100542–100542. 3 indexed citations

Williams, Hannah, Hannah M. Thompson, Christina Y. Lee, et al.. (2024). Assessing Endoscopic Response in Locally Advanced Rectal Cancer Treated with Total Neoadjuvant Therapy: Development and Validation of a Highly Accurate Convolutional Neural Network. Annals of Surgical Oncology. 31(10). 6443–6451. 4 indexed citations

Paudyal, Ramesh, Jue Jiang, James E. Han, et al.. (2024). Auto-segmentation of neck nodal metastases using self-distilled masked image transformer on longitudinal MR images. PubMed. 1(1). ubae004–ubae004.

Jiang, Jue, et al.. (2024). Self‐supervised learning improves robustness of deep learning lung tumor segmentation models to CT imaging differences. Medical Physics. 52(3). 1573–1588. 1 indexed citations

Paudyal, Ramesh, Akash Shah, Oğuz Akın, et al.. (2023). Artificial Intelligence in CT and MR Imaging for Oncological Applications. Cancers. 15(9). 2573–2573. 57 indexed citations

Jiang, Jingting, et al.. (2023). Clinical Feasibility of Deep Learning-Based CT during Treatment CBCT Tumor Registration-Segmentation in Thoracic Radiotherapy (RT). International Journal of Radiation Oncology*Biology*Physics. 117(2). e656–e656. 1 indexed citations

Jiang, Jue, et al.. (2022). Self-supervised 3D Anatomy Segmentation Using Self-distilled Masked Image Transformer (SMIT). Lecture notes in computer science. 13434. 556–566. 20 indexed citations

10.

Huang, Raymond Y., Robert J. Young, Benjamin M. Ellingson, et al.. (2020). Volumetric analysis of IDH-mutant lower-grade glioma: a natural history study of tumor growth rates before and after treatment. Neuro-Oncology. 22(12). 1822–1830. 26 indexed citations

11.

Apte, Aditya, Aditi Iyer, Maria Thor, et al.. (2020). Library of deep-learning image segmentation and outcomes model-implementations. Physica Medica. 73. 190–196. 17 indexed citations

12.

Beer, Lucian, Hilal Şahin, Nicholas W. Bateman, et al.. (2020). Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis. European Radiology. 30(8). 4306–4316. 29 indexed citations

13.

Um, Hyemin, et al.. (2019). Impact of image preprocessing on the scanner dependence of multi-parametric MRI radiomic features and covariate shift in multi-institutional glioblastoma datasets. Physics in Medicine and Biology. 64(16). 165011–165011. 84 indexed citations

14.

Vargas, Hebert Alberto, Harini Veeraraghavan, Maura Miccò, et al.. (2017). A novel representation of inter-site tumour heterogeneity from pre-treatment computed tomography textures classifies ovarian cancers by clinical outcome. European Radiology. 27(9). 3991–4001. 88 indexed citations

15.

Fehr, Duc, C. Ross Schmidtlein, Sinchun Hwang, Joseph O. Deasy, & Harini Veeraraghavan. (2016). Automatic detection and tracking of longitudinal changes of multiple bone metastases from dual energy CT. PubMed. 2016. 168–171. 3 indexed citations

16.

Wibmer, Andreas G., Hedvig Hricak, Tatsuo Gondo, et al.. (2015). Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. European Radiology. 25(10). 2840–2850. 321 indexed citations breakdown →

17.

Sharp, G, Karl Fritscher, Vladimír Pekar, et al.. (2014). Vision 20/20: Perspectives on automated image segmentation for radiotherapy. Medical Physics. 41(5). 50902–50902. 284 indexed citations breakdown →

18.

Veeraraghavan, Harini & James V. Miller. (2013). Faceted Visualization of Three Dimensional Neuroanatomy By Combining Ontology with Faceted Search. Neuroinformatics. 12(2). 245–259. 3 indexed citations

19.

Veeraraghavan, Harini & Manuela Veloso. (2008). Teaching sequential tasks with repetition through demonstration. Adaptive Agents and Multi-Agents Systems. 1357–1360. 9 indexed citations

20.

Veeraraghavan, Harini, Stefan Atev, Nathaniel Bird, Paul Schrater, & Nikolaos Papanikolopoulos. (2005). IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. 22 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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