Alvina Goh
- Radiology, Nuclear Medicine and Imaging top 5%
- Computer Vision and Pattern Recognition top 5%
- Cognitive Neuroscience top 10%
- Artificial Intelligence top 10%
- Pediatrics, Perinatology and Child Health top 10%
- Co-authors
- Renè VidalPaul M. ThompsonChristophe LengletMarco ReisertTing‐Shuo YoBen JeurissenFatima TensaoutiSylvain Gouttard
- Topics
- Advanced Neuroimaging Techniques and Applications (7 papers)Morphological variations and asymmetry (5 papers)MRI in cancer diagnosis (4 papers)
- Cited by
- Computational MathematicsRadiology, Nuclear Medicine and ImagingComputer Vision and Pattern Recognition
- Partner nations
- SingaporeUnited StatesFrance
In The Last Decade
Alvina Goh
16 papers receiving 704 citations
Peers
Comparison fields: 5 of 82
- Radiology, Nuclear Medicine and Imaging 397
- Computer Vision and Pattern Recognition 215
- Cognitive Neuroscience 135
- Artificial Intelligence 106
- Pediatrics, Perinatology and Child Health 95
Countries citing papers authored by Alvina Goh
This map shows the geographic impact of Alvina Goh'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 Alvina Goh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alvina Goh more than expected).
Fields of papers citing papers by Alvina Goh
This network shows the impact of papers produced by Alvina Goh. 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 Alvina Goh. The network helps show where Alvina Goh may publish in the future.
Co-authorship network of co-authors of Alvina Goh
This figure shows the co-authorship network connecting the top 25 collaborators of Alvina Goh. A scholar is included among the top collaborators of Alvina Goh 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 Alvina Goh. Alvina Goh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 11 | |
| 3 | 33 | |
| 4 | 1 | |
| 5 | 13 | |
| 6 | 36 | |
| 7 | Visual tracking with generative template model based on Riemannian manifold of covariances | 9 |
| 8 | 38 | |
| 9 | 9 | |
| 10 | 9 | |
| 11 | 3 | |
| 12 | 291 | |
| 13 | 44 | |
| 14 | 17 | |
| 15 | 70 | |
| 16 | 137 |
About Alvina Goh
Alvina Goh is a scholar working on Geometry and Topology, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition, having authored 16 papers that have together received 723 indexed citations. Recurring topics across this work include Advanced Neuroimaging Techniques and Applications (7 papers), Morphological variations and asymmetry (5 papers) and MRI in cancer diagnosis (4 papers). The work is most often cited by research in Computational Mathematics (31 citations), Radiology, Nuclear Medicine and Imaging (397 citations) and Computer Vision and Pattern Recognition (215 citations). Alvina Goh has collaborated with scholars based in Singapore, United States and France. Frequent co-authors include Renè Vidal, Paul M. Thompson, Christophe Lenglet, Marco Reisert, Ting‐Shuo Yo, Ben Jeurissen, Fatima Tensaouti, Sylvain Gouttard, Maxime Descoteaux and James G. Malcolm. Their work appears in journals such as NeuroImage, Human Brain Mapping and IEEE Journal of Selected Topics in Signal Processing.
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.