Tal Arbel

12.5k total citations · 1 hit paper
97 papers, 2.8k citations indexed

About

Tal Arbel is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Tal Arbel has authored 97 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 65 papers in Computer Vision and Pattern Recognition, 22 papers in Artificial Intelligence and 22 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Tal Arbel's work include Medical Image Segmentation Techniques (37 papers), Advanced Image and Video Retrieval Techniques (18 papers) and Brain Tumor Detection and Classification (16 papers). Tal Arbel is often cited by papers focused on Medical Image Segmentation Techniques (37 papers), Advanced Image and Video Retrieval Techniques (18 papers) and Brain Tumor Detection and Classification (16 papers). Tal Arbel collaborates with scholars based in Canada, United States and United Kingdom. Tal Arbel's co-authors include M. Jorge Cardoso, D. Louis Collins, Matthew Toews, Douglas L. Arnold, Frank P. Ferrie, Nagesh K. Subbanna, Doina Precup, James J. Clark, Catherine Laporte and Dante De Nigris and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Tal Arbel

95 papers receiving 2.7k 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.

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

2017 970 citations M. Jorge Cardoso, Tal Arbel et al. Lecture notes in computer science profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tal Arbel Canada	25	1.5k	987	661	394	355	97	2.8k
Ender Konukoğlu Switzerland	33	1.7k 1.2×	1.2k 1.2×	868 1.3×	734 1.9×	455 1.3×	102	3.8k
Ismail Ben Ayed Canada	28	1.8k 1.2×	1.0k 1.0×	810 1.2×	383 1.0×	231 0.7×	136	3.1k
Chenyang Xu China	13	1.5k 1.0×	612 0.6×	461 0.7×	364 0.9×	237 0.7×	31	2.3k
Grégoire Malandain France	36	2.3k 1.6×	1.6k 1.7×	377 0.6×	844 2.1×	210 0.6×	138	4.9k
Marc Niethammer United States	33	2.2k 1.5×	1.7k 1.7×	1.3k 2.0×	590 1.5×	337 0.9×	179	5.1k
Qiaowei Zhang China	14	881 0.6×	825 0.8×	512 0.8×	436 1.1×	261 0.7×	35	2.1k
Peng Cao China	26	618 0.4×	1.1k 1.1×	416 0.6×	316 0.8×	175 0.5×	142	2.4k
Ali Gholipour United States	31	986 0.7×	1.6k 1.6×	742 1.1×	189 0.5×	223 0.6×	136	3.8k
Carmel Hayes United Kingdom	19	2.0k 1.4×	3.1k 3.2×	393 0.6×	794 2.0×	174 0.5×	43	5.1k
Supun Samarasekera United States	23	1.7k 1.2×	519 0.5×	294 0.4×	286 0.7×	117 0.3×	82	2.6k

Countries citing papers authored by Tal Arbel

Since Specialization

Citations

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

Fields of papers citing papers by Tal Arbel

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tal Arbel

This figure shows the co-authorship network connecting the top 25 collaborators of Tal Arbel. A scholar is included among the top collaborators of Tal Arbel 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 Tal Arbel. Tal Arbel 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

Nichyporuk, Brennan, et al.. (2025). Improving Robustness and Reliability in Medical Image Classification With Latent-Guided Diffusion and Nested-Ensembles. IEEE Transactions on Medical Imaging. 44(12). 4890–4902.

Arbel, Tal, et al.. (2025). The role of AI for MRI-analysis in multiple sclerosis—A brief overview. Frontiers in Artificial Intelligence. 8. 1478068–1478068. 2 indexed citations

Nichyporuk, Brennan, Francesca Bovis, Maria Pia Sormani, et al.. (2022). Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning. Nature Communications. 13(1). 5645–5645. 20 indexed citations

Arnold, Douglas L., et al.. (2020). CNN Detection of New and Enlarging Multiple Sclerosis Lesions from Longitudinal Mri Using Subtraction Images. 127–130. 15 indexed citations

Subbanna, Nagesh K., Deepthi Rajashekar, Bastian Cheng, et al.. (2019). Stroke Lesion Segmentation in FLAIR MRI Datasets Using Customized Markov Random Fields. Frontiers in Neurology. 10. 541–541. 22 indexed citations

Müller, Henning, B. Michael Kelm, Tal Arbel, et al.. (2017). Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging. Lecture notes in computer science. 19 indexed citations

Verhey, Leonard H., Colm Elliott, Helen M. Branson, et al.. (2014). Rate of Agreement for Manual and Automated Techniques for Determination of New T2 Lesions in Children with Multiple Sclerosis and Acute Demyelination (P2.242). Neurology. 82(10_supplement). 1 indexed citations

Elliott, Colm, Douglas L. Arnold, D. Louis Collins, & Tal Arbel. (2013). Temporally Consistent Probabilistic Detection of New Multiple Sclerosis Lesions in Brain MRI. IEEE Transactions on Medical Imaging. 32(8). 1490–1503. 52 indexed citations

Karimaghaloo, Zahra, Hassan Rivaz, Douglas L. Arnold, D. Louis Collins, & Tal Arbel. (2013). Adaptive Voxel, Texture and Temporal Conditional Random Fields for Detection of Gad-Enhancing Multiple Sclerosis Lesions in Brain MRI. Lecture notes in computer science. 16(Pt 3). 543–550. 7 indexed citations

10.

Subbanna, Nagesh K., Simon J. Francis, Doina Precup, et al.. (2011). Adapted MRF Segmentation of Multiple Sclerosis Lesions Using Local Contextual Information.. 351–356. 1 indexed citations

11.

Laporte, Catherine & Tal Arbel. (2010). Learning to estimate out-of-plane motion in ultrasound imagery of real tissue. Medical Image Analysis. 15(2). 202–213. 17 indexed citations

12.

Nigris, Dante De, Laurence Mercier, Rolando F. Del Maestro, D. Louis Collins, & Tal Arbel. (2010). Hierarchical Multimodal Image Registration Based on Adaptive Local Mutual Information. Lecture notes in computer science. 13(Pt 2). 643–651. 8 indexed citations

13.

Laporte, Catherine & Tal Arbel. (2008). Combinatorial and Probabilistic Fusion of Noisy Correlation Measurements for Untracked Freehand 3-D Ultrasound. IEEE Transactions on Medical Imaging. 27(7). 984–994. 16 indexed citations

14.

Brooks, Rupert, Tal Arbel, & Doina Precup. (2007). Fast image alignment using anytime algorithms. International Joint Conference on Artificial Intelligence. 2078–2083. 3 indexed citations

15.

Toews, Matthew & Tal Arbel. (2007). A Statistical Parts-Based Model of Anatomical Variability. IEEE Transactions on Medical Imaging. 26(4). 497–508. 28 indexed citations

16.

Laporte, Catherine & Tal Arbel. (2007). Probabilistic Speckle Decorrelation for 3D Ultrasound. Lecture notes in computer science. 10(Pt 1). 925–932. 4 indexed citations

17.

Harmouche, Rola, D. Louis Collins, Douglas L. Arnold, Simon Francis, & Tal Arbel. (2006). Bayesian MS Lesion Classification Modeling Regional and Local Spatial Information. 984–987. 28 indexed citations

18.

Toews, Matthew, D. Louis Collins, & Tal Arbel. (2006). A Statistical Parts-Based Appearance Model of Inter-subject Variability. Lecture notes in computer science. 9(Pt 1). 232–240. 4 indexed citations

19.

Toews, Matthew, D. Louis Collins, & Tal Arbel. (2005). Maximum a Posteriori Local Histogram Estimation for Image Registration. Lecture notes in computer science. 8(Pt 2). 163–170. 10 indexed citations

20.

Arbel, Tal, Xavier Morandi, Roch M. Comeau, & D. Louis Collins. (2004). Automatic non-linear MRI-ultrasound registration for the correction of intra-operative brain deformations. Computer Aided Surgery. 9(4). 123–136. 49 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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