Dongang Wang

1.4k total citations
8 papers, 48 citations indexed

About

Dongang Wang is a scholar working on Molecular Biology, Pathology and Forensic Medicine and Artificial Intelligence. According to data from OpenAlex, Dongang Wang has authored 8 papers receiving a total of 48 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Molecular Biology, 3 papers in Pathology and Forensic Medicine and 3 papers in Artificial Intelligence. Recurrent topics in Dongang Wang's work include Multiple Sclerosis Research Studies (3 papers), AI in cancer detection (2 papers) and Cutaneous Melanoma Detection and Management (2 papers). Dongang Wang is often cited by papers focused on Multiple Sclerosis Research Studies (3 papers), AI in cancer detection (2 papers) and Cutaneous Melanoma Detection and Management (2 papers). Dongang Wang collaborates with scholars based in Australia, United States and France. Dongang Wang's co-authors include Michael Barnett, Chenyu Wang, Chun‐Chien Shieh, Wanli Ouyang, Lei Bai, Mariano Cabezas, Shih‐Fu Chang, Weidong Cai, Clare R. Voss and Hongzhi Li and has published in prestigious journals such as Neurology, Frontiers in Neuroscience and Frontiers in Neurology.

In The Last Decade

Dongang Wang

8 papers receiving 47 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dongang Wang Australia	5	21	13	10	9	6	8	48
Emmanuel Vallée France	2	17 0.8×	13 1.0×	14 1.4×	12 1.3×	2 0.3×	3	45
Alexander Kalinovsky Belarus	4	22 1.0×	16 1.2×	20 2.0×	4 0.4×	2 0.3×	5	58
Ahmed Harouni United States	5	17 0.8×	16 1.2×	26 2.6×	3 0.3×	4 0.7×	6	73
Jayashree Kalpathy-Cramer United States	4	15 0.7×	10 0.8×	15 1.5×	14 1.6×	3 0.5×	10	41
Leonardo O. Iheme Türkiye	5	54 2.6×	16 1.2×	26 2.6×	11 1.2×	2 0.3×	13	82
Elli Kanal United States	3	32 1.5×	8 0.6×	5 0.5×	2 0.2×	6 1.0×	3	48
Mohammad Al Sa’d United Kingdom	3	28 1.3×	26 2.0×	14 1.4×	15 1.7×		4	58
Tingyao Li China	3	14 0.7×	7 0.5×	42 4.2×	6 0.7×	2 0.3×	4	66
Ashish Arora United States	6	41 2.0×	33 2.5×	20 2.0×	4 0.4×	16 2.7×	12	83
Shenghua Cheng China	7	54 2.6×	46 3.5×	20 2.0×	9 1.0×	20 3.3×	17	115

Countries citing papers authored by Dongang Wang

Since Specialization

Citations

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

Fields of papers citing papers by Dongang Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dongang Wang

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

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Barnett, Michael, John DE Parratt, Con Yiannikas, et al.. (2025). Evolution of Chronic Lesion Tissue in Relapsing-Remitting Patients With Multiple Sclerosis. Neurology Neuroimmunology & Neuroinflammation. 12(3). e200377–e200377. 2 indexed citations

2.

Almgren, Hannes, et al.. (2024). Associations of high-sensitivity C-reactive protein with neuropsychological outcomes and cerebral white matter hyperintensities in older adults at risk of dementia. Brain Behavior & Immunity - Health. 43. 100924–100924. 1 indexed citations

3.

Bai, Lei, Dongang Wang, Michael Barnett, et al.. (2024). Improving multiple sclerosis lesion segmentation across clinical sites: A federated learning approach with noise-resilient training. Artificial Intelligence in Medicine. 152. 102872–102872. 10 indexed citations

4.

Liu, Dongnan, Mariano Cabezas, Dongang Wang, et al.. (2023). Multiple sclerosis lesion segmentation: revisiting weighting mechanisms for federated learning. Frontiers in Neuroscience. 17. 1167612–1167612. 12 indexed citations

5.

Wang, Dongang, et al.. (2023). Real world validation of an AI-based CT hemorrhage detection tool. Frontiers in Neurology. 14. 1177723–1177723. 9 indexed citations

6.

Wang, Dongang, Mariano Cabezas, Lei Bai, et al.. (2023). Learning from pseudo-labels: deep networks improve consistency in longitudinal brain volume estimation. Frontiers in Neuroscience. 17. 1196087–1196087. 4 indexed citations

7.

Liu, Dongnan, Mariano Cabezas, Dongang Wang, et al.. (2022). DAMS-Net: A Domain Adaptive Lesion Segmentation Framework in Patients with Multiple Sclerosis from Multiple Imaging Centers (P18-4.001). Neurology. 98(18_supplement). 1 indexed citations

8.

Lu, Di, Clare R. Voss, Fangbo Tao, et al.. (2016). Cross-media Event Extraction and Recommendation. 72–76. 9 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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