Xiaohua Qian

2.4k total citations
82 papers, 1.7k citations indexed

About

Xiaohua Qian is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Xiaohua Qian has authored 82 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Radiology, Nuclear Medicine and Imaging, 19 papers in Artificial Intelligence and 18 papers in Computer Vision and Pattern Recognition. Recurrent topics in Xiaohua Qian's work include Radiomics and Machine Learning in Medical Imaging (22 papers), AI in cancer detection (15 papers) and Pancreatic and Hepatic Oncology Research (14 papers). Xiaohua Qian is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (22 papers), AI in cancer detection (15 papers) and Pancreatic and Hepatic Oncology Research (14 papers). Xiaohua Qian collaborates with scholars based in China, United States and United Kingdom. Xiaohua Qian's co-authors include Vicki L. Rothman, George P. Tuszynski, Thomas J. Wang, Chencheng Zhang, Rui Guo, Xiaozhu Lin, Roberto F. Nicosia, Jun Li, Hua Tan and Xueyan Li and has published in prestigious journals such as Nature Communications, IEEE Transactions on Image Processing and IEEE Access.

In The Last Decade

Xiaohua Qian

77 papers receiving 1.6k citations

Peers

Xiaohua Qian

RNMI

CVPR

Phedias Diamandis Canada

Daisuke Komura Japan

Yi Gao China

Atsushi Teramoto Japan

Wenjun Liao China

Jingjing Gao China

Masayuki Tsuneki Japan

Georgios Z. Papadakis United States

Peter Chang United States

Phedias Diamandis Canada

Xiaohua Qian

320 ×0.5

RNMI

571 ×1.5

362 ×1.1

123 ×0.4

CVPR

222 ×0.8

Citations per year, relative to Xiaohua Qian Xiaohua Qian (= 1×) peers Phedias Diamandis

Countries citing papers authored by Xiaohua Qian

Since Specialization

Citations

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

Fields of papers citing papers by Xiaohua Qian

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaohua Qian

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

Guo, Rui, et al.. (2025). Causality-driven candidate identification for reliable DNA methylation biomarker discovery. Nature Communications. 16(1). 680–680. 2 indexed citations

Xiao, Xiang, et al.. (2024). A causality-inspired generalized model for automated pancreatic cancer diagnosis. Medical Image Analysis. 94. 103154–103154. 6 indexed citations

Guo, Rui, et al.. (2024). A causal counterfactual graph neural network for arising-from-chair abnormality detection in parkinsonians. Medical Image Analysis. 97. 103266–103266. 3 indexed citations

Shen, Ruinan, et al.. (2024). Graph Convolutional Network for AD and MCI Diagnosis Utilizing Peripheral DNA Methylation: Réseau de neurones en graphes pour le diagnostic de la MA et du TCL à l’aide de la méthylation de l’ADN périphérique. The Canadian Journal of Psychiatry. 69(12). 869–879.

Guo, Rui, Zheng Xie, Chencheng Zhang, & Xiaohua Qian. (2024). Causality-Enhanced Multiple Instance Learning With Graph Convolutional Networks for Parkinsonian Freezing-of-Gait Assessment. IEEE Transactions on Image Processing. 33. 3991–4001. 3 indexed citations

Guo, Rui, Linbin Wang, Chencheng Zhang, et al.. (2024). A Causality-Informed Graph Convolutional Network for Video Assessment of Parkinsonian Leg Agility. IEEE Transactions on Circuits and Systems for Video Technology. 34(11). 11101–11114. 1 indexed citations

Qian, Xiaohua, Hua Tan, Weiling Zhao, et al.. (2024). Radiogenomics-Based Risk Prediction of Glioblastoma Multiforme with Clinical Relevance. Genes. 15(6). 718–718. 3 indexed citations

Bai, Haiqing, T. Marshall, Longlong Si, et al.. (2023). Rapid Prototyping of Thermoplastic Microfluidic 3D Cell Culture Devices by Creating Regional Hydrophilicity Discrepancy. Advanced Science. 11(7). e2304332–e2304332. 4 indexed citations

Wang, Weishen, et al.. (2023). A dual-transformation with contrastive learning framework for lymph node metastasis prediction in pancreatic cancer. Medical Image Analysis. 85. 102753–102753. 12 indexed citations

10.

Guo, Rui, Hao Li, Chencheng Zhang, & Xiaohua Qian. (2022). A tree-structure-guided graph convolutional network with contrastive learning for the assessment of parkinsonian hand movements. Medical Image Analysis. 81. 102560–102560. 15 indexed citations

11.

Dong, Zhenglin, Min He, Tao Chen, et al.. (2022). Differential diagnosis of pancreatic cystic neoplasms through a radiomics-assisted system. Frontiers in Oncology. 12. 941744–941744. 5 indexed citations

12.

Li, Jun, et al.. (2021). Model-Driven Deep Learning Method for Pancreatic Cancer Segmentation Based on Spiral-Transformation. IEEE Transactions on Medical Imaging. 41(1). 75–87. 25 indexed citations

13.

Lin, Xiaozhu, et al.. (2020). Combined Spiral Transformation and Model-Driven Multi-Modal Deep Learning Scheme for Automatic Prediction of TP53 Mutation in Pancreatic Cancer. IEEE Transactions on Medical Imaging. 40(2). 735–747. 32 indexed citations

14.

Yang, Yan, et al.. (2020). miR-485-5p improves the progression of ovarian cancer by targeting SRC in vitro and in vivo. Neoplasma. 67(5). 1022–1031. 11 indexed citations

15.

Tan, Hua, Shan Huang, Zhigang Zhang, et al.. (2019). Pan-cancer analysis on microRNA-associated gene activation. EBioMedicine. 43. 82–97. 57 indexed citations

16.

Qian, Xiaohua, Hua Tan, Jian Zhang, et al.. (2016). Stratification of pseudoprogression and true progression of glioblastoma multiform based on longitudinal diffusion tensor imaging without segmentation. Medical Physics. 43(11). 5889–5902. 27 indexed citations

17.

Qian, Xiaohua, Hua Tan, Jian Zhang, et al.. (2015). Objective classification system for sagittal craniosynostosis based on suture segmentation. Medical Physics. 42(9). 5545–5558. 13 indexed citations

18.

Li, Shuhua, Xiaohua Qian, Zhengan Yuan, et al.. (2014). Molecular epidemiology of measles virus infection in Shanghai in 2000–2012: the first appearance of genotype D8. The Brazilian Journal of Infectious Diseases. 18(6). 581–590. 6 indexed citations

19.

Li, Wei, et al.. (2014). Association of interleukin-4 genetic polymorphisms with sporadic Alzheimer's disease in Chinese Han population. Neuroscience Letters. 563. 17–21. 15 indexed citations

20.

Qian, Xiaohua, et al.. (2013). An active contour model for medical image segmentation with application to brain CT image. Medical Physics. 40(2). 21911–21911. 45 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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