Feng Yang

2.9k total citations · 2 hit papers
81 papers, 2.0k citations indexed

About

Feng Yang is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Media Technology. According to data from OpenAlex, Feng Yang has authored 81 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Radiology, Nuclear Medicine and Imaging, 31 papers in Computer Vision and Pattern Recognition and 11 papers in Media Technology. Recurrent topics in Feng Yang's work include Radiomics and Machine Learning in Medical Imaging (20 papers), COVID-19 diagnosis using AI (16 papers) and Advanced Neuroimaging Techniques and Applications (10 papers). Feng Yang is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (20 papers), COVID-19 diagnosis using AI (16 papers) and Advanced Neuroimaging Techniques and Applications (10 papers). Feng Yang collaborates with scholars based in China, United States and France. Feng Yang's co-authors include Caiyun Yang, Mu Zhou, Wei Shen, Jie Tian, Jie Tian, Dongdong Yu, Di Dong, Yali Zang, Stefan Jaeger and Sameer Antani and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Image Processing and Cerebral Cortex.

In The Last Decade

Feng Yang

75 papers receiving 1.9k citations

Hit Papers

align trajectories

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.

Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification

2016 461 citations Feng Yang, Caiyun Yang et al. profile →
Multi-scale Convolutional Neural Networks for Lung Nodule Classification

2015 408 citations Feng Yang, Caiyun Yang et al. Lecture notes in computer science profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Feng Yang China	18	1.2k	640	546	502	197	81	2.0k
Stergios Christodoulidis Switzerland	14	841 0.7×	432 0.7×	332 0.6×	601 1.2×	204 1.0×	22	1.6k
Marios Anthimopoulos Switzerland	14	807 0.7×	395 0.6×	474 0.9×	608 1.2×	363 1.8×	20	2.0k
Jiantao Pu United States	28	1.2k 1.1×	948 1.5×	631 1.2×	404 0.8×	163 0.8×	104	2.2k
Ali Madani United States	14	773 0.7×	180 0.3×	305 0.6×	619 1.2×	242 1.2×	17	2.5k
Muhammad Salman Khan Pakistan	20	1.8k 1.6×	392 0.6×	530 1.0×	1.4k 2.7×	286 1.5×	119	3.2k
Chung‐Ming Chen Taiwan	24	1.1k 0.9×	470 0.7×	452 0.8×	652 1.3×	261 1.3×	127	2.5k
Yuchen Qiu United States	18	1.1k 0.9×	271 0.4×	331 0.6×	859 1.7×	238 1.2×	62	1.8k
Jianpeng Zhang China	21	1.5k 1.3×	604 0.9×	684 1.3×	1.5k 3.0×	240 1.2×	63	3.0k
Masahiro Oda Japan	26	913 0.8×	557 0.9×	559 1.0×	372 0.7×	309 1.6×	180	3.3k
Michaël Aertsen Belgium	16	646 0.6×	203 0.3×	732 1.3×	604 1.2×	192 1.0×	61	2.0k

Countries citing papers authored by Feng Yang

Since Specialization

Citations

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

Fields of papers citing papers by Feng Yang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Feng Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Feng Yang. A scholar is included among the top collaborators of Feng Yang 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 Feng Yang. Feng Yang 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:

20 of 20 papers shown

Yang, Feng, Mengya Wang, Shengli Li, et al.. (2025). Evaluation of microgaps and microleakage at the implant-abutment interface of individualized abutments in the maxillary anterior region under functional loading: A pragmatic clinical trial. Journal of Prosthodontic Research. 69(1). 58–67. 1 indexed citations

Yang, Feng, et al.. (2025). 3D-GSW: 3D Gaussian Splatting for Robust Watermarking. 5938–5948.

Rajaraman, Sivaramakrishnan, Ghada Zamzmi, Feng Yang, et al.. (2024). Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images. SHILAP Revista de lepidopterología. 3(1). e0000286–e0000286. 2 indexed citations

Rajaraman, Sivaramakrishnan, Ghada Zamzmi, Feng Yang, et al.. (2024). Semantically redundant training data removal and deep model classification performance: A study with chest X-rays. Computerized Medical Imaging and Graphics. 115. 102379–102379. 2 indexed citations

Yang, Feng, Ghada Zamzmi, Sivaramakrishnan Rajaraman, et al.. (2023). Assessing Inter-Annotator Agreement for Medical Image Segmentation. IEEE Access. 11. 21300–21312. 31 indexed citations

Rajaraman, Sivaramakrishnan, Ghada Zamzmi, Feng Yang, Zhiyun Xue, & Sameer Antani. (2023). Data Characterization for Reliable AI in Medicine. Communications in computer and information science. 1704. 3–11. 4 indexed citations

Rajaraman, Sivaramakrishnan, Feng Yang, Ghada Zamzmi, Zhiyun Xue, & Sameer Antani. (2023). Can deep adult lung segmentation models generalize to the pediatric population?. Expert Systems with Applications. 229(Pt A). 120531–120531. 6 indexed citations

Xue, Zhiyun, Feng Yang, Sivaramakrishnan Rajaraman, Ghada Zamzmi, & Sameer Antani. (2023). Cross Dataset Analysis of Domain Shift in CXR Lung Region Detection. Diagnostics. 13(6). 1068–1068. 5 indexed citations

Liang, Zhaohui, Zhiyun Xue, Sivaramakrishnan Rajaraman, Feng Yang, & Sameer Antani. (2023). Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning. Lecture notes in computer science. 14307. 128–137. 1 indexed citations

10.

Rajaraman, Sivaramakrishnan, Feng Yang, Ghada Zamzmi, Zhiyun Xue, & Sameer Antani. (2023). Assessing the Impact of Image Resolution on Deep Learning for TB Lesion Segmentation on Frontal Chest X-rays. Diagnostics. 13(4). 747–747. 3 indexed citations

11.

Wang, Lihui, Hong Yao, Yongbin Qin, et al.. (2022). Connecting macroscopic diffusion metrics of cardiac diffusion tensor imaging and microscopic myocardial structures based on simulation. Medical Image Analysis. 77. 102325–102325. 2 indexed citations

12.

Zhu, Yuemin, Pei Niu, Ting Su, et al.. (2021). Super-Energy-Resolution Material Decomposition for Spectral Photon-Counting CT Using Pixel-Wise Learning. IEEE Access. 9. 168485–168495. 2 indexed citations

13.

Kassim, Yasmin M., Feng Yang, Hang Yu, Richard J. Maude, & Stefan Jaeger. (2021). Diagnosing Malaria Patients with Plasmodium falciparum and vivax Using Deep Learning for Thick Smear Images. Diagnostics. 11(11). 1994–1994. 34 indexed citations

14.

Yang, Feng, Hang Yu, Manohar Karki, et al.. (2021). Differentiating between drug-sensitive and drug-resistant tuberculosis with machine learning for clinical and radiological features. Quantitative Imaging in Medicine and Surgery. 12(1). 675–687. 17 indexed citations

15.

Yang, Feng, Yuemin Zhu, Pierre‐Simon Jouk, et al.. (2018). Quantitative comparison of human myocardial fiber orientations derived from DTI and polarized light imaging. Physics in Medicine and Biology. 63(21). 215003–215003. 10 indexed citations

16.

Luo, Jianhua, Binjie Qin, Wanqing Li, et al.. (2017). Fast single image super-resolution using estimated low-frequency k-space data in MRI. Magnetic Resonance Imaging. 40. 1–11. 8 indexed citations

17.

Shen, Weifeng, et al.. (2015). Automatic localization of vertebrae based on convolutional neural networks. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9413. 94132E–94132E. 13 indexed citations

18.

Mu, Wei, Zhe Chen, Ying Liang, et al.. (2015). Staging of cervical cancer based on tumor heterogeneity characterized by texture features on¹⁸F-FDG PET images. Physics in Medicine and Biology. 60(13). 5123–5139. 69 indexed citations

19.

Yang, Feng. (2011). Method of Key Frame Extraction Based on Sub-Shot Clustering. Transactions of Beijing Institute of Technology. 1 indexed citations

20.

Yang, Feng, et al.. (2011). Feature-based interpolation of diffusion tensor fields and application to human cardiac DT-MRI. Medical Image Analysis. 16(2). 459–481. 24 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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