Ning Mao

2.7k total citations
137 papers, 1.8k citations indexed

About

Ning Mao is a scholar working on Radiology, Nuclear Medicine and Imaging, Cognitive Neuroscience and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Ning Mao has authored 137 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 80 papers in Radiology, Nuclear Medicine and Imaging, 32 papers in Cognitive Neuroscience and 28 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Ning Mao's work include Radiomics and Machine Learning in Medical Imaging (63 papers), Functional Brain Connectivity Studies (31 papers) and Advanced X-ray and CT Imaging (24 papers). Ning Mao is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (63 papers), Functional Brain Connectivity Studies (31 papers) and Advanced X-ray and CT Imaging (24 papers). Ning Mao collaborates with scholars based in China, United States and South Korea. Ning Mao's co-authors include Haizhu Xie, Nan Hong, Heng Ma, Shaofeng Duan, Chao Sun, Jiangfen Wu, Ping Yin, Ping Yin, Meijie Liu and Ying‐Hong Shi and has published in prestigious journals such as NeuroImage, Scientific Reports and Biological Psychiatry.

In The Last Decade

Ning Mao

128 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ning Mao China 22 1.1k 393 386 298 221 137 1.8k
Xi Zhang China 30 970 0.9× 409 1.0× 90 0.2× 144 0.5× 36 0.2× 119 3.2k
Guolin Ma China 19 430 0.4× 122 0.3× 163 0.4× 216 0.7× 153 0.7× 94 1.3k
Huan Lin China 18 365 0.3× 163 0.4× 165 0.4× 54 0.2× 57 0.3× 45 839
Elisa Cuadrado‐Godia Spain 35 490 0.5× 1.2k 3.0× 126 0.3× 97 0.3× 53 0.2× 110 3.4k
Christian Salvatore Italy 22 634 0.6× 119 0.3× 438 1.1× 179 0.6× 348 1.6× 51 2.0k
Mário Luiz Ribeiro Monteiro Brazil 34 982 0.9× 228 0.6× 204 0.5× 195 0.7× 44 0.2× 232 3.7k
Xiaoying Wang China 22 533 0.5× 615 1.6× 108 0.3× 205 0.7× 25 0.1× 135 1.8k
Francesca Gallivanone Italy 21 947 0.9× 331 0.8× 237 0.6× 206 0.7× 207 0.9× 51 1.9k
Xu Yan China 27 2.1k 2.0× 507 1.3× 135 0.3× 232 0.8× 287 1.3× 102 3.1k
Thomas Schmitz Germany 20 420 0.4× 212 0.5× 91 0.2× 29 0.1× 15 0.1× 73 1.8k

Countries citing papers authored by Ning Mao

Since Specialization
Citations

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

Fields of papers citing papers by Ning Mao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ning Mao

This figure shows the co-authorship network connecting the top 25 collaborators of Ning Mao. A scholar is included among the top collaborators of Ning Mao 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 Ning Mao. Ning Mao 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.
Chu, Tongpeng, Xiaopeng Si, Xicheng Song, et al.. (2025). Understanding structural-functional connectivity coupling in patients with major depressive disorder: A white matter perspective. Journal of Affective Disorders. 373. 219–226. 1 indexed citations
2.
Mao, Ning, et al.. (2024). DBEF-Net: Diffusion-Based Boundary-Enhanced Fusion Network for medical image segmentation. Expert Systems with Applications. 255. 124467–124467. 7 indexed citations
3.
Guo, Yuting, Tongpeng Chu, Heng Ma, et al.. (2024). Diagnosis of Major Depressive Disorder Based on Individualized Brain Functional and Structural Connectivity. Journal of Magnetic Resonance Imaging. 61(4). 1712–1725. 5 indexed citations
4.
Mao, Ning, et al.. (2024). BADM: Boundary-Assisted Diffusion Model for Skin Lesion Segmentation. Engineering Applications of Artificial Intelligence. 137. 109213–109213. 5 indexed citations
5.
Luo, Yao, Min Cao, Kun Cao, et al.. (2024). Malignancy risk stratification prediction of BI-RADS 4B calcifications based on contrast-enhanced mammographic features: a multicenter study. Breast Cancer Research and Treatment. 210(1). 135–145. 1 indexed citations
6.
Zhao, Feng, Fan Feng, Xiaobo Chen, et al.. (2024). Multi-head self-attention mechanism-based global feature learning model for ASD diagnosis. Biomedical Signal Processing and Control. 91. 106090–106090. 15 indexed citations
7.
Zhang, Haicheng, Fan Lin, Tiantian Zheng, et al.. (2024). Artificial intelligence-based classification of breast lesion from contrast enhanced mammography: a multicenter study. International Journal of Surgery. 110(5). 2593–2603. 5 indexed citations
8.
Zhang, Rui, Wei Xia, Ning Mao, et al.. (2024). Prediction of axillary lymph node metastasis using a magnetic resonance imaging radiomics model of invasive breast cancer primary tumor. Cancer Imaging. 24(1). 122–122. 5 indexed citations
9.
Che, Kaili, Heng Ma, Feng Zhao, et al.. (2023). Diagnosis of Major Depressive Disorder Using Machine Learning Based on Multisequence MRI Neuroimaging Features. Journal of Magnetic Resonance Imaging. 58(5). 1420–1430. 13 indexed citations
10.
11.
Mao, Ning, et al.. (2023). Multimodal and multiscale evidence for network-based cortical thinning in major depressive disorder. NeuroImage. 277. 120265–120265. 7 indexed citations
12.
Zhao, Feng, Mingli Zhang, Ke Lv, et al.. (2023). Multi-classifier fusion based on belief-value for the diagnosis of autism spectrum disorder. Frontiers in Human Neuroscience. 17. 1257987–1257987.
13.
Wang, Cai, et al.. (2023). Artificial intelligence–based prediction of cervical lymph node metastasis in papillary thyroid cancer with CT. European Radiology. 33(10). 6828–6840. 22 indexed citations
14.
Wang, Shixiong, et al.. (2023). Efficacy of ultrasound-guided radiofrequency ablation of papillary thyroid microcarcinoma. Asian Journal of Surgery. 47(1). 350–353. 3 indexed citations
15.
Li, Yuna, Tongpeng Chu, Yaou Liu, et al.. (2022). Classification of major depression disorder via using minimum spanning tree of individual high-order morphological brain network. Journal of Affective Disorders. 323. 10–20. 7 indexed citations
16.
Zhao, Feng, et al.. (2021). Constructing Multi-View High-Order Functional Connectivity Networks for Diagnosis of Autism Spectrum Disorder. IEEE Transactions on Biomedical Engineering. 69(3). 1237–1250. 20 indexed citations
17.
Dai, Yi, Ping Yin, Ning Mao, et al.. (2020). Differentiation of Pelvic Osteosarcoma and Ewing Sarcoma Using Radiomic Analysis Based on T2‐Weighted Images and Contrast‐Enhanced T1‐Weighted Images. BioMed Research International. 2020(1). 9078603–9078603. 27 indexed citations
18.
Mao, Ning, et al.. (2020). Contrast-Enhanced Spectral Mammography Versus Ultrasonography: Diagnostic Performance in Symptomatic Patients with Dense Breasts. Korean Journal of Radiology. 21(4). 442–442. 16 indexed citations
19.
Mao, Ning, Ke Zhang, Miao Zhang, et al.. (2020). Analysis of the variation pattern in left upper division veins and establishment of simplified vein models for anatomical segmentectomy. Annals of Translational Medicine. 8(22). 1515–1515. 13 indexed citations
20.
Mao, Ning, Ping Yin, Qinglin Wang, et al.. (2018). Added Value of Radiomics on Mammography for Breast Cancer Diagnosis: A Feasibility Study. Journal of the American College of Radiology. 16(4). 485–491. 65 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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