Mobarakol Islam

1.8k citations
75 papers · 931 · h-index 19

Impact in

Papers in

Mobarakol Islam

70 papers receiving 913 citations

Peers

Mobarakol Islam
Comparison fields: 5 of 88
  • Health Informatics 59
  • Computer Vision and Pattern Recognition 392
  • Neurology 129
  • Radiology, Nuclear Medicine and Imaging 193
  • Artificial Intelligence 272
Replace Jai Prashanth Rao with:
Jai Prashanth Rao Singapore
Oliver Burgert Germany
Justin Ker Singapore
Zhijian Song China
Matthew Sinclair United Kingdom
Mohammad Hesam Hesamian Malaysia
Xinglong Wu China
Erik Smistad Norway
Stefanie Demirci Germany
Zekuan Yu China
Mobarakol Islam relative to Jai Prashanth Rao Singapore Jai Prashanth Rao's profile →
Citations per field
00.5×3.3×
Jai Prashanth Rao · 1×
Citations per year

Countries citing papers authored by Mobarakol Islam

Since Specialization
Citations

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

Fields of papers citing papers by Mobarakol Islam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Mobarakol Islam, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mobarakol Islam Line = papers co-authored together Mobarakol Islam links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 75 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202087
2 202148
3 202134
4 202034
5 202032
6 202032
7 202329
8 202429
9 202129
10 202328
11 202228
12 202028
13 202228
14 202427
15 202124
16 202123
17 201922
18 201920
19 201819
20 202317

About Mobarakol Islam

Mobarakol Islam is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Biomedical Engineering, Radiology, Nuclear Medicine and Imaging and Health Informatics, having authored 75 papers that have together received 931 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (15 papers), Multimodal Machine Learning Applications (14 papers), Radiomics and Machine Learning in Medical Imaging (10 papers), Advanced Neural Network Applications (8 papers), Artificial Intelligence in Healthcare and Education (8 papers), Surgical Simulation and Training (7 papers), Medical Image Segmentation Techniques (7 papers) and Anatomy and Medical Technology (7 papers). The work is most often cited by research in Health Informatics (59 citations), Computer Vision and Pattern Recognition (392 citations), Neurology (129 citations), Radiology, Nuclear Medicine and Imaging (193 citations) and Artificial Intelligence (272 citations). Mobarakol Islam has collaborated with scholars based in United Kingdom, Hong Kong and Singapore. Frequent co-authors include Hongliang Ren, Mengya Xu, An Wang, Chwee Ming Lim, Ben Glocker, Long Bai, Long Bai, Beilei Cui, Dulani Meedeniya and Indika Perera. Their work appears in journals such as International Journal of Computer Assisted Radiology and Surgery, Lecture notes in computer science, IEEE Robotics and Automation Letters, IEEE Transactions on Medical Imaging and IEEE Transactions on Automation Science and Engineering.

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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