Rahil Garnavi

3.7k total citations
52 papers, 1.4k citations indexed

About

Rahil Garnavi is a scholar working on Oncology, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Rahil Garnavi has authored 52 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Oncology, 19 papers in Computer Vision and Pattern Recognition and 17 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Rahil Garnavi's work include Cutaneous Melanoma Detection and Management (21 papers), AI in cancer detection (14 papers) and Retinal Imaging and Analysis (14 papers). Rahil Garnavi is often cited by papers focused on Cutaneous Melanoma Detection and Management (21 papers), AI in cancer detection (14 papers) and Retinal Imaging and Analysis (14 papers). Rahil Garnavi collaborates with scholars based in Australia, United States and Iran. Rahil Garnavi's co-authors include M. Aldeen, Dwarikanath Mahapatra, Behzad Bozorgtabar, Bhavna Antony, Suman Sedai, James Bailey, Gadi Wollstein, Hiroshi Ishikawa, Joel S. Schuman and Yasmeen George and has published in prestigious journals such as PLoS ONE, Expert Systems with Applications and IBM Journal of Research and Development.

In The Last Decade

Rahil Garnavi

52 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rahil Garnavi Australia 20 564 558 486 454 319 52 1.4k
Lei Bi Australia 20 689 1.2× 508 0.9× 707 1.5× 402 0.9× 72 0.2× 70 1.5k
Irene Fondón Spain 16 526 0.9× 250 0.4× 408 0.8× 224 0.5× 140 0.4× 32 936
Balázs Harangi Hungary 14 320 0.6× 469 0.8× 367 0.8× 331 0.7× 301 0.9× 51 948
Dehui Xiang China 21 126 0.2× 1.3k 2.2× 441 0.9× 885 1.9× 526 1.6× 100 2.0k
Weifang Zhu China 23 130 0.2× 1.5k 2.7× 420 0.9× 903 2.0× 805 2.5× 124 2.3k
Yuhui Ma China 14 99 0.2× 692 1.2× 279 0.6× 573 1.3× 262 0.8× 36 1.4k
Sertan Serte Cyprus 15 106 0.2× 733 1.3× 447 0.9× 263 0.6× 139 0.4× 36 1.0k
Adrián Colomer Spain 16 114 0.2× 440 0.8× 323 0.7× 293 0.6× 226 0.7× 56 796
Qiangguo Jin China 10 81 0.1× 703 1.3× 355 0.7× 663 1.5× 262 0.8× 32 1.3k
John Arévalo Colombia 13 121 0.2× 483 0.9× 705 1.5× 367 0.8× 45 0.1× 30 1.0k

Countries citing papers authored by Rahil Garnavi

Since Specialization
Citations

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

Fields of papers citing papers by Rahil Garnavi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rahil Garnavi

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

All Works

20 of 20 papers shown
1.
Yu, Hsin‐Hao, Stefan Maetschke, Bhavna Antony, et al.. (2020). Estimating Global Visual Field Indices in Glaucoma by Combining Macula and Optic Disc OCT Scans Using 3-Dimensional Convolutional Neural Networks. Ophthalmology Glaucoma. 4(1). 102–112. 33 indexed citations
2.
Maetschke, Stefan, Bhavna Antony, Hiroshi Ishikawa, et al.. (2019). A feature agnostic approach for glaucoma detection in OCT volumes. PLoS ONE. 14(7). e0219126–e0219126. 134 indexed citations
3.
Antony, Bhavna, Stefan Maetschke, & Rahil Garnavi. (2019). Automated summarisation of SDOCT volumes using deep learning: Transfer learning vs de novo trained networks. PLoS ONE. 14(5). e0203726–e0203726. 4 indexed citations
4.
Mahapatra, Dwarikanath, Suman Sedai, & Rahil Garnavi. (2018). Elastic Registration of Medical Images With GANs.. arXiv (Cornell University). 8 indexed citations
5.
Antony, Bhavna, Matthew H. Lee, Hiroshi Ishikawa, et al.. (2018). Retinal optical coherence tomography image enhancement via deep learning. Biomedical Optics Express. 9(12). 6205–6205. 78 indexed citations
6.
George, Yasmeen, M. Aldeen, & Rahil Garnavi. (2018). Psoriasis image representation using patch-based dictionary learning for erythema severity scoring. Computerized Medical Imaging and Graphics. 66. 44–55. 21 indexed citations
7.
Mahapatra, Dwarikanath, Behzad Bozorgtabar, & Rahil Garnavi. (2018). Image super-resolution using progressive generative adversarial networks for medical image analysis. Computerized Medical Imaging and Graphics. 71. 30–39. 181 indexed citations
8.
Aldeen, M., et al.. (2018). Biologically Inspired QuadTree Color Detection in Dermoscopy Images of Melanoma. IEEE Journal of Biomedical and Health Informatics. 23(2). 570–577. 22 indexed citations
9.
Ge, Zongyuan, Sergey Demyanov, Behzad Bozorgtabar, et al.. (2017). Exploiting local and generic features for accurate skin lesions classification using clinical and dermoscopy imaging. 986–990. 44 indexed citations
10.
Roy, Pallab, Ruwan Tennakoon, Suman Sedai, et al.. (2017). A novel hybrid approach for severity assessment of Diabetic Retinopathy in colour fundus images. 1078–1082. 25 indexed citations
11.
Mahapatra, Dwarikanath, et al.. (2016). A CNN based neurobiology inspired approach for retinal image quality assessment. PubMed. 2016. 1304–1307. 10 indexed citations
12.
Abedini, Mani, et al.. (2015). A Cloud-Based Infrastructure for Feedback-Driven Training and Image Recognition. Studies in health technology and informatics. 216. 691–5. 3 indexed citations
13.
Liang, Xi, et al.. (2015). Automatic segmentation of the left ventricle into 17 anatomical regions in cardiac MR imaging. PubMed. 2015. 6531–6535. 5 indexed citations
14.
Chen, Qiang, Mani Abedini, Rahil Garnavi, & Xi Liang. (2014). IBM Research Australia at LifeCLEF2014: Plant Identification Task.. CLEF (Working Notes). 693–704. 9 indexed citations
15.
Garnavi, Rahil, M. Aldeen, & James Bailey. (2012). Computer-Aided Diagnosis of Melanoma Using Border- and Wavelet-Based Texture Analysis. IEEE Transactions on Information Technology in Biomedicine. 16(6). 1239–1252. 136 indexed citations
16.
Garnavi, Rahil & M. Aldeen. (2011). Optimized Weighted Performance Index for Objective Evaluation of Border-Detection Methods in Dermoscopy Images. IEEE Transactions on Information Technology in Biomedicine. 15(6). 908–917. 4 indexed citations
17.
Garnavi, Rahil, M. Aldeen, M. Emre Celebi, George Varigos, & Sue Finch. (2010). Border detection in dermoscopy images using hybrid thresholding on optimized color channels. Computerized Medical Imaging and Graphics. 35(2). 105–115. 124 indexed citations
18.
Garnavi, Rahil, M. Aldeen, & M. Emre Celebi. (2010). Weighted performance index for objective evaluation of border detection methods in dermoscopy images. Skin Research and Technology. 17(1). 35–44. 15 indexed citations
19.
Garnavi, Rahil, et al.. (2009). Skin Lesion Segmentation Using Color Channel Optimization And Clustering-Based Histogram Thresholding. Zenodo (CERN European Organization for Nuclear Research). 5 indexed citations
20.
Garnavi, Rahil, et al.. (2008). Texture Analysis in Lung HRCT Images. Espace ÉTS (ETS). 2. 305–311. 6 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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