Tarek M. Taha

2.1k total citations · 1 hit paper
3 papers, 586 citations indexed

About

Tarek M. Taha is a scholar working on Dermatology, Computer Vision and Pattern Recognition and Oncology. According to data from OpenAlex, Tarek M. Taha has authored 3 papers receiving a total of 586 indexed citations (citations by other indexed papers that have themselves been cited), including 1 paper in Dermatology, 1 paper in Computer Vision and Pattern Recognition and 1 paper in Oncology. Recurrent topics in Tarek M. Taha's work include AI in cancer detection (1 paper), Nonmelanoma Skin Cancer Studies (1 paper) and Retinal Imaging and Analysis (1 paper). Tarek M. Taha is often cited by papers focused on AI in cancer detection (1 paper), Nonmelanoma Skin Cancer Studies (1 paper) and Retinal Imaging and Analysis (1 paper). Tarek M. Taha collaborates with scholars based in United States. Tarek M. Taha's co-authors include Vijayan K. Asari, Md Zahangir Alom, Mahmudul Hasan, Chris Yakopcic, Qing Wu and Mark Barnell and has published in prestigious journals such as Electronics and Journal of Medical Imaging.

In The Last Decade

Tarek M. Taha

3 papers receiving 570 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.

Recurrent residual U-Net for medical image segmentation

2019 569 citations Md Zahangir Alom, Chris Yakopcic et al. Journal of Medical Imaging profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tarek M. Taha United States	3	286	283	193	74	73	3	586
Sheng Lian China	11	380 1.3×	588 2.1×	276 1.4×	77 1.0×	152 2.1×	23	996
Dominik Müller Germany	6	270 0.9×	141 0.5×	173 0.9×	20 0.3×	63 0.9×	16	540
Pierre-Henri Conze France	14	304 1.1×	277 1.0×	199 1.0×	64 0.9×	68 0.9×	47	616
Kamil Říha Czechia	14	237 0.8×	328 1.2×	119 0.6×	115 1.6×	22 0.3×	66	665
Adrián Colomer Spain	16	440 1.5×	293 1.0×	323 1.7×	226 3.1×	27 0.4×	56	796
Salman Khan United Arab Emirates	6	299 1.0×	244 0.9×	249 1.3×	17 0.2×	100 1.4×	18	742
Yicheng Wu China	12	325 1.1×	392 1.4×	151 0.8×	130 1.8×	72 1.0×	32	775
Iñaki Soto‐Rey Germany	7	219 0.8×	107 0.4×	143 0.7×	19 0.3×	53 0.7×	28	521
Haonan Wang China	6	254 0.9×	464 1.6×	269 1.4×	20 0.3×	129 1.8×	12	748

Countries citing papers authored by Tarek M. Taha

Since Specialization

Citations

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

Fields of papers citing papers by Tarek M. Taha

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tarek M. Taha. 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 Tarek M. Taha. The network helps show where Tarek M. Taha may publish in the future.

Co-authorship network of co-authors of Tarek M. Taha

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

All Works

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3 of 3 papers shown

1.

Yakopcic, Chris, et al.. (2024). Survey of Deep Learning Accelerators for Edge and Emerging Computing. Electronics. 13(15). 2988–2988. 3 indexed citations

2.

Alom, Md Zahangir, et al.. (2020). Skin cancer segmentation and classification with improved deep convolutional neural network. 36–36. 14 indexed citations

3.

Alom, Md Zahangir, Chris Yakopcic, Mahmudul Hasan, Tarek M. Taha, & Vijayan K. Asari. (2019). Recurrent residual U-Net for medical image segmentation. Journal of Medical Imaging. 6(1). 1–1. 569 indexed citations breakdown →

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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