Yaniv Gal

665 total citations
46 papers, 494 citations indexed

About

Yaniv Gal is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Genetics. According to data from OpenAlex, Yaniv Gal has authored 46 papers receiving a total of 494 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Radiology, Nuclear Medicine and Imaging, 10 papers in Computer Vision and Pattern Recognition and 7 papers in Genetics. Recurrent topics in Yaniv Gal's work include Medical Imaging Techniques and Applications (9 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and MRI in cancer diagnosis (8 papers). Yaniv Gal is often cited by papers focused on Medical Imaging Techniques and Applications (9 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and MRI in cancer diagnosis (8 papers). Yaniv Gal collaborates with scholars based in Australia, United States and Italy. Yaniv Gal's co-authors include ‪Stuart Crozier‬, E Philippe, Stephen Rose, Paul Thomas, Nicholas Dowson, Andrew P. Bradley, Andrew Mehnert, Christopher J. Bell, Simon Puttick and Dominic Kennedy and has published in prestigious journals such as NeuroImage, Cancer and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Yaniv Gal

43 papers receiving 472 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yaniv Gal Australia 11 240 140 83 73 63 46 494
Turid Torheim United Kingdom 11 284 1.2× 74 0.5× 26 0.3× 60 0.8× 42 0.7× 16 449
J. Pearlman United States 8 148 0.6× 248 1.8× 54 0.7× 79 1.1× 187 3.0× 9 500
Anahita Fathi Kazerooni United States 16 396 1.6× 209 1.5× 35 0.4× 71 1.0× 58 0.9× 65 606
Sofie Van Cauter Belgium 13 513 2.1× 229 1.6× 17 0.2× 82 1.1× 30 0.5× 22 718
Zhongping Chen China 10 163 0.7× 111 0.8× 14 0.2× 38 0.5× 58 0.9× 34 393
Michelle Bardis United States 8 370 1.5× 178 1.3× 41 0.5× 92 1.3× 151 2.4× 12 576
Ulf Neuberger Germany 10 492 2.0× 285 2.0× 27 0.3× 79 1.1× 142 2.3× 15 761
Niall Moore United Kingdom 11 325 1.4× 26 0.2× 57 0.7× 111 1.5× 58 0.9× 20 548
Paula de Robles Canada 8 172 0.7× 285 2.0× 74 0.9× 33 0.5× 103 1.6× 18 567
Keith Robson United Kingdom 11 116 0.5× 131 0.9× 42 0.5× 23 0.3× 143 2.3× 24 378

Countries citing papers authored by Yaniv Gal

Since Specialization
Citations

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

Fields of papers citing papers by Yaniv Gal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yaniv Gal

This figure shows the co-authorship network connecting the top 25 collaborators of Yaniv Gal. A scholar is included among the top collaborators of Yaniv Gal 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 Yaniv Gal. Yaniv Gal 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.
Primiero, Clare, Brigid Betz‐Stablein, Yaniv Gal, et al.. (2025). Multi‐task AI models in dermatology: Overcoming critical clinical translation challenges for enhanced skin lesion diagnosis. Journal of the European Academy of Dermatology and Venereology. 39(12). 2121–2133. 1 indexed citations
2.
Yu, Zhen, Lie Ju, Yaniv Gal, et al.. (2025). Hierarchical skin lesion image classification with prototypical decision tree. npj Digital Medicine. 8(1). 26–26.
3.
4.
Choupan, Jeiran, Pamela K. Douglas, Yaniv Gal, et al.. (2020). Temporal embedding and spatiotemporal feature selection boost multi-voxel pattern analysis decoding accuracy. Journal of Neuroscience Methods. 345. 108836–108836. 2 indexed citations
5.
Pagnozzi, Alex M., Simona Fiori, Roslyn N. Boyd, et al.. (2015). Optimization of MRI-based scoring scales of brain injury severity in children with unilateral cerebral palsy. Pediatric Radiology. 46(2). 270–279. 7 indexed citations
6.
Bell, Christopher J., Nicholas Dowson, Simon Puttick, et al.. (2015). Increasing feasibility and utility of 18 F-FDOPA PET for the management of glioma. Nuclear Medicine and Biology. 42(10). 788–795. 40 indexed citations
7.
Bell, Christopher J., Nicholas Dowson, Mike Fay, et al.. (2015). Hypoxia Imaging in Gliomas With 18F-Fluoromisonidazole PET: Toward Clinical Translation. Seminars in Nuclear Medicine. 45(2). 136–150. 54 indexed citations
8.
Sepehrband, Farshid, Jeiran Choupan, Emmanuel Caruyer, et al.. (2015). lop-DWI: A Novel Scheme for Pre-Processing of Diffusion-Weighted Images in the Gradient Direction Domain. Frontiers in Neurology. 5. 290–290. 5 indexed citations
9.
Gal, Yaniv, et al.. (2014). An algorithm for microscopic specimen delineation and focus candidate selection. Micron. 66. 51–62. 3 indexed citations
10.
Bell, Christopher J., Stephen Rose, Simon Puttick, et al.. (2014). Dual acquisition of18F-FMISO and18F-FDOPA. Physics in Medicine and Biology. 59(14). 3925–3949. 7 indexed citations
11.
Neubert, Aleš, Jürgen Fripp, Craig Engstrom, et al.. (2014). Validity and reliability of computerized measurement of lumbar intervertebral disc height and volume from magnetic resonance images. The Spine Journal. 14(11). 2773–2781. 15 indexed citations
12.
Dowson, Nicholas, Paul Thomas, Michael Fay, et al.. (2014). Early Prediction of Treatment Response in Advanced Gliomas with 18F-dopa Positron-Emission Tomography. Current Oncology. 21(1). 172–178. 7 indexed citations
13.
Bell, Christopher J., Kerstin Pannek, Paul Thomas, et al.. (2013). Distance informed Track-Weighted Imaging (diTWI): A framework for sensitising streamline information to neuropathology. NeuroImage. 86. 60–66. 3 indexed citations
14.
Gal, Yaniv, Stephen Rose, Pierrick Bourgeat, et al.. (2012). Automatic classification of high grade brain tumour MRI for improved resection and therapy planning. Queensland's institutional digital repository (The University of Queensland). 10. 204–11. 1 indexed citations
15.
Rose, Stephen, Michael Fay, Paul Thomas, et al.. (2012). Correlation of MRI-Derived Apparent Diffusion Coefficients in Newly Diagnosed Gliomas with [18F]-Fluoro-L-Dopa PET: What Are We Really Measuring with Minimum ADC?. American Journal of Neuroradiology. 34(4). 758–764. 49 indexed citations
16.
Gal, Yaniv, Andrew Mehnert, Andrew P. Bradley, Dominic Kennedy, & ‪Stuart Crozier‬. (2011). New Spatiotemporal Features for Improved Discrimination of Benign and Malignant Lesions in Dynamic Contrast-Enhanced-Magnetic Resonance Imaging of the Breast. Journal of Computer Assisted Tomography. 35(5). 645–652. 8 indexed citations
17.
18.
Gal, Yaniv, Matthew Browne, Andrew D. Short, et al.. (2010). A new system for breakzone location and the measurement of breaking wave heights and periods.. Griffith Research Online (Griffith University, Queensland, Australia). 110. 2234–2236. 6 indexed citations
19.
Gal, Yaniv, et al.. (2009). Denoising of Dynamic Contrast-Enhanced MR Images Using Dynamic Nonlocal Means. IEEE Transactions on Medical Imaging. 29(2). 302–310. 56 indexed citations
20.
Gal, Yaniv, et al.. (1954). A propos de trois cas de tumeurs ovariennes dysgénetiques primitives à caractères métanéphrogenes.. 60. 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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