Ruobing Huang

738 total citations
29 papers, 316 citations indexed

About

Ruobing Huang is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ruobing Huang has authored 29 papers receiving a total of 316 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 11 papers in Radiology, Nuclear Medicine and Imaging and 8 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ruobing Huang's work include AI in cancer detection (10 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Domain Adaptation and Few-Shot Learning (8 papers). Ruobing Huang is often cited by papers focused on AI in cancer detection (10 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Domain Adaptation and Few-Shot Learning (8 papers). Ruobing Huang collaborates with scholars based in China, United Kingdom and Hong Kong. Ruobing Huang's co-authors include Dong Ni, J. Alison Noble, Xin Yang, Zehui Lin, Haoran Dou, Weidi Xie, Wenwen Xu, Zihan Mei, Yijie Dong and Jianqiao Zhou and has published in prestigious journals such as Nature Communications, NeuroImage and Expert Systems with Applications.

In The Last Decade

Ruobing Huang

26 papers receiving 314 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ruobing Huang China 10 167 162 88 58 33 29 316
Benjamin Hou United Kingdom 8 111 0.7× 131 0.8× 77 0.9× 48 0.8× 17 0.5× 16 310
Mostafa Ghelich Oghli Iran 9 98 0.6× 237 1.5× 97 1.1× 41 0.7× 26 0.8× 19 386
Amir Alansary United States 9 80 0.5× 119 0.7× 121 1.4× 90 1.6× 23 0.7× 17 319
Maysam Shahedi United States 11 127 0.8× 215 1.3× 110 1.3× 38 0.7× 42 1.3× 37 435
Xindi Hu China 5 83 0.5× 97 0.6× 114 1.3× 13 0.2× 28 0.8× 12 267
Seyed Raein Hashemi United States 6 86 0.5× 90 0.6× 99 1.1× 36 0.6× 43 1.3× 7 242
Awais Mansoor United States 11 70 0.4× 118 0.7× 58 0.7× 35 0.6× 13 0.4× 33 305
Helena R. Torres Portugal 10 69 0.4× 106 0.7× 140 1.6× 40 0.7× 12 0.4× 47 302
Khalaf Alshamrani Saudi Arabia 8 76 0.5× 119 0.7× 48 0.5× 12 0.2× 54 1.6× 23 284
Bishesh Khanal United Kingdom 8 69 0.4× 71 0.4× 68 0.8× 39 0.7× 10 0.3× 25 248

Countries citing papers authored by Ruobing Huang

Since Specialization
Citations

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

Fields of papers citing papers by Ruobing Huang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ruobing Huang

This figure shows the co-authorship network connecting the top 25 collaborators of Ruobing Huang. A scholar is included among the top collaborators of Ruobing Huang 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 Ruobing Huang. Ruobing Huang 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.
Huang, Ruobing, et al.. (2025). AviationCopilot: Building a reliable LLM-based Aviation Copilot inspired by human pilot training. Advanced Engineering Informatics. 69. 103806–103806. 1 indexed citations
2.
Huang, Yuhao, Allan Chang, Haoran Dou, et al.. (2025). Flip Learning: Weakly supervised erase to segment nodules in breast ultrasound. Medical Image Analysis. 102. 103552–103552. 1 indexed citations
3.
Yang, Xin, Huaiyu Wu, Xiliang Zhu, et al.. (2025). Artificial Intelligence in Breast US Diagnosis and Report Generation. Radiology Artificial Intelligence. 7(4). e240625–e240625.
4.
Huang, Ruobing, Yinyu Ye, Allan Chang, et al.. (2025). Subtyping breast lesions via collective intelligence based long-tailed recognition in ultrasound. Medical Image Analysis. 102. 103548–103548. 1 indexed citations
5.
Zhang, Haoming, Yan Bai, Qianhong Liang, et al.. (2025). Deep learning approach for screening neonatal cerebral lesions on ultrasound in China. Nature Communications. 16(1). 7778–7778.
6.
7.
Huang, Ruobing, et al.. (2024). Multi-purposed diagnostic system for ovarian endometrioma using CNN and transformer networks in ultrasound. Biomedical Signal Processing and Control. 91. 105923–105923. 5 indexed citations
8.
Chang, Allan, Tao Xing, Yuhao Huang, et al.. (2024). P2ED: A four-quadrant framework for progressive prompt enhancement in 3D interactive medical imaging segmentation. Neural Networks. 183. 106973–106973. 1 indexed citations
9.
Lin, Zehui, Lian Li, Ruobing Huang, et al.. (2024). Biopsy or Follow-up: AI Improves the Clinical Strategy of US BI-RADS 4A Breast Nodules Using a Convolutional Neural Network. Clinical Breast Cancer. 24(5). e319–e332.e2. 5 indexed citations
10.
Peng, Jiayu, et al.. (2023). One-Stop Automated Diagnostic System for Carpal Tunnel Syndrome in Ultrasound Images Using Deep Learning. Ultrasound in Medicine & Biology. 50(2). 304–314. 3 indexed citations
11.
Huang, Ruobing, et al.. (2023). Untrained deep learning-based phase retrieval for fringe projection profilometry. Optics and Lasers in Engineering. 164. 107483–107483. 16 indexed citations
12.
Yang, Xin, Yuhao Huang, Zehui Lin, et al.. (2022). HASA: Hybrid architecture search with aggregation strategy for echinococcosis classification and ovary segmentation in ultrasound images. Expert Systems with Applications. 202. 117242–117242. 11 indexed citations
13.
Huang, Ruobing, Zehui Lin, Zijie Zheng, et al.. (2022). Extracting keyframes of breast ultrasound video using deep reinforcement learning. Medical Image Analysis. 80. 102490–102490. 24 indexed citations
14.
Zhou, Xinrui, Xu Zhou, Shengfeng Liu, et al.. (2022). Artificial Intelligence‐Assisted Ultrasound Diagnosis on Infant Developmental Dysplasia of the Hip Under Constrained Computational Resources. Journal of Ultrasound in Medicine. 42(6). 1235–1248. 7 indexed citations
15.
Zhou, Jianqiao, Xiaohong Jia, Zehui Lin, et al.. (2022). Multi-Attribute Attention Network for Interpretable Diagnosis of Thyroid Nodules in Ultrasound Images. IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control. 69(9). 2611–2620. 20 indexed citations
16.
Moser, Felipe, et al.. (2022). BEAN: Brain Extraction and Alignment Network for 3D Fetal Neurosonography. NeuroImage. 258. 119341–119341. 12 indexed citations
17.
Yang, Xin, Haoran Dou, Ruobing Huang, et al.. (2021). Agent With Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound. IEEE Transactions on Medical Imaging. 40(7). 1950–1961. 20 indexed citations
18.
Huang, Ruobing, Weidi Xie, & J. Alison Noble. (2018). VP-Nets : Efficient automatic localization of key brain structures in 3D fetal neurosonography. Medical Image Analysis. 47. 127–139. 34 indexed citations
19.
Huang, Ruobing, Ana I. L. Namburete, & J. Alison Noble. (2018). Learning to segment key clinical anatomical structures in fetal neurosonography informed by a region-based descriptor. Journal of Medical Imaging. 5(1). 1–1. 9 indexed citations
20.
Huang, Ruobing, Ana I. L. Namburete, Mohammad Yaqub, & J. Alison Noble. (2015). Automated Mid-sagittal Plane Selection for Corpus Callosum Visualization in 3D Ultrasound Images.. 46–51. 3 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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