Dijia Wu

1.7k total citations · 1 hit paper
28 papers, 1.0k citations indexed

About

Dijia Wu is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Dijia Wu has authored 28 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Radiology, Nuclear Medicine and Imaging, 10 papers in Biomedical Engineering and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Dijia Wu's work include Cardiac Imaging and Diagnostics (6 papers), Advanced X-ray and CT Imaging (6 papers) and Radiomics and Machine Learning in Medical Imaging (5 papers). Dijia Wu is often cited by papers focused on Cardiac Imaging and Diagnostics (6 papers), Advanced X-ray and CT Imaging (6 papers) and Radiomics and Machine Learning in Medical Imaging (5 papers). Dijia Wu collaborates with scholars based in China, United States and Germany. Dijia Wu's co-authors include Dinggang Shen, Feng Shi, Liming Xia, Huan Yuan, Fuhua Yan, He Sui, Yaozong Gao, Bin Song, Zhanhao Mo and Ying Wei and has published in prestigious journals such as Nature Communications, Radiology and IEEE Transactions on Medical Imaging.

In The Last Decade

Dijia Wu

25 papers receiving 982 citations

Hit Papers

Multi-scale and multi-parametric radiomics of gadoxetate ... 2021 2026 2022 2024 2021 50 100 150
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.

  1. Multi-scale and multi-parametric radiomics of gadoxetate disodium–enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma ≤ 5 cm
    2021 152 citations Huanhuan Chong, Li Yang et al. European Radiology profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dijia Wu China 13 752 354 162 144 129 28 1.0k
Eugene Vorontsov Canada 10 742 1.0× 353 1.0× 162 1.0× 176 1.2× 195 1.5× 14 1.2k
Gabriel Chartrand Canada 14 823 1.1× 379 1.1× 193 1.2× 184 1.3× 268 2.1× 19 1.6k
Xinglong Wu China 8 338 0.4× 250 0.7× 80 0.5× 72 0.5× 96 0.7× 37 703
Ge-Ge Wu China 9 479 0.6× 318 0.9× 93 0.6× 103 0.7× 24 0.2× 10 710
He Ma China 20 756 1.0× 460 1.3× 224 1.4× 44 0.3× 270 2.1× 72 1.2k
Michał Byra Poland 15 817 1.1× 723 2.0× 65 0.4× 48 0.3× 220 1.7× 42 1.3k
Theresa Thai United States 12 519 0.7× 331 0.9× 126 0.8× 47 0.3× 170 1.3× 32 1.0k
Avishek Chatterjee Netherlands 17 815 1.1× 340 1.0× 283 1.7× 139 1.0× 77 0.6× 38 1.2k

Countries citing papers authored by Dijia Wu

Since Specialization
Citations

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

Fields of papers citing papers by Dijia Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dijia Wu

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

All Works

20 of 20 papers shown
1.
Wang, Yining, et al.. (2025). A topology-preserving three-stage framework for fully-connected coronary artery extraction. Medical Image Analysis. 103. 103578–103578.
2.
Zhang, Jiayin, Yanli Song, Qingqi Hong, et al.. (2025). Deciphering age- and sex-specific patterns of coronary artery atherosclerosis from a large Chinese cohort. Nature Communications. 16(1). 10616–10616.
3.
Wu, Dijia, Yiqiang Zhan, Xiaomei Zhu, et al.. (2025). Deep Learning–based Quantitative CT Myocardial Perfusion Imaging and Risk Stratification of Coronary Artery Disease. Radiology. 315(1). e242570–e242570. 2 indexed citations
4.
Zhang, Yu, Zijun Song, Zhanhao Mo, et al.. (2024). Enhancing Radiologists’ Performance in Detecting Cerebral Aneurysms Using a Deep Learning Model: A Multicenter Study. Academic Radiology. 32(3). 1611–1620. 2 indexed citations
5.
Wu, Dijia, et al.. (2024). Deep learning model for low-dose CT late iodine enhancement imaging and extracellular volume quantification. European Radiology. 35(7). 3871–3882. 4 indexed citations
6.
Li, Yuehua, Xifu Wang, Yanli Song, et al.. (2024). Deep learning reconstruction for coronary CT angiography in patients with origin anomaly, stent or bypass graft. La radiologia medica. 129(8). 1173–1183. 6 indexed citations
7.
8.
Zhang, Xiao, Dijia Wu, Jiameng Liu, et al.. (2023). An Anatomy- and Topology-Preserving Framework for Coronary Artery Segmentation. IEEE Transactions on Medical Imaging. 43(2). 723–733. 18 indexed citations
9.
Wang, Shuhao, et al.. (2022). Assessment of automatic rib fracture detection on chest CT using a deep learning algorithm. European Radiology. 33(3). 1824–1834. 20 indexed citations
10.
Cao, Xiaohuan, Dijia Wu, Lei Chen, et al.. (2022). Evaluation of an automated intracranial aneurysm detection and rupture analysis approach using cascade detection and classification networks. Computerized Medical Imaging and Graphics. 102. 102126–102126. 17 indexed citations
11.
Liu, Xiang, Dijia Wu, Huihui Xie, et al.. (2021). Clinical evaluation of AI software for rib fracture detection and its impact on junior radiologist performance. Acta Radiologica. 63(11). 1535–1545. 9 indexed citations
12.
Sheng, Ruofan, Jing Huang, Weiguo Zhang, et al.. (2021). A Semi-Automatic Step-by-Step Expert-Guided LI-RADS Grading System Based on Gadoxetic Acid-Enhanced MRI. Journal of Hepatocellular Carcinoma. Volume 8. 671–683. 4 indexed citations
13.
Chong, Huanhuan, Li Yang, Ruofan Sheng, et al.. (2021). Multi-scale and multi-parametric radiomics of gadoxetate disodium–enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma ≤ 5 cm. European Radiology. 31(7). 4824–4838. 152 indexed citations breakdown →
14.
Xuan, Kai, Lichi Zhang, Zhong Xue, et al.. (2021). Reducing magnetic resonance image spacing by learning without ground-truth. Pattern Recognition. 120. 108103–108103. 5 indexed citations
15.
Xia, Liming, Fuhua Yan, Feng Shi, et al.. (2020). Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning. IEEE Transactions on Medical Imaging. 39(8). 2606–2614. 172 indexed citations
16.
Sun, Liang, Zhanhao Mo, Fuhua Yan, et al.. (2020). Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT. IEEE Journal of Biomedical and Health Informatics. 24(10). 2798–2805. 165 indexed citations
17.
Yang, Yongquan, et al.. (2020). FTBME: feature transferring based multi-model ensemble. Multimedia Tools and Applications. 79(25-26). 18767–18799. 13 indexed citations
18.
Sofka, Michal, Dijia Wu, Michael Sühling, et al.. (2011). Automatic Contrast Phase Estimation in CT Volumes. Lecture notes in computer science. 14(Pt 3). 166–174. 5 indexed citations
19.
Wu, Dijia, Jinbo Bi, & Kim L. Boyer. (2009). A min-max framework of cascaded classifier with multiple instance learning for computer aided diagnosis. 2009 IEEE Conference on Computer Vision and Pattern Recognition. 1359–1366. 7 indexed citations
20.
Shi, Cong, et al.. (2003). Turbo product codes on frequency selective fading channel. 3. 1233–1236.

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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