Peng Tang

1.1k total citations
22 papers, 593 citations indexed

About

Peng Tang is a scholar working on Computer Vision and Pattern Recognition, Oncology and Artificial Intelligence. According to data from OpenAlex, Peng Tang has authored 22 papers receiving a total of 593 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 6 papers in Oncology and 6 papers in Artificial Intelligence. Recurrent topics in Peng Tang's work include Cutaneous Melanoma Detection and Management (6 papers), AI in cancer detection (5 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Peng Tang is often cited by papers focused on Cutaneous Melanoma Detection and Management (6 papers), AI in cancer detection (5 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Peng Tang collaborates with scholars based in China, Germany and United Kingdom. Peng Tang's co-authors include Shao Xiang, Xintong Yan, Qiaokang Liang, Dan Zhang, Gianmarc Coppola, Wei Sun, Nan Yang, Tobias Lasser, Sebastian Krammer and Mi Wang and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Medical Imaging and Pattern Recognition.

In The Last Decade

Peng Tang

22 papers receiving 579 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Peng Tang China 11 283 279 156 119 112 22 593
Fengying Xie China 14 355 1.3× 509 1.8× 269 1.7× 157 1.3× 53 0.5× 23 839
Jyoti Kini India 16 495 1.7× 69 0.2× 279 1.8× 95 0.8× 326 2.9× 69 828
Ebrahim Nasr-Esfahani Iran 7 263 0.9× 292 1.0× 154 1.0× 92 0.8× 141 1.3× 10 527
Tarek M. Taha United States 3 193 0.7× 59 0.2× 283 1.8× 29 0.2× 286 2.6× 3 586
F. Joel W.-M. Leong United Kingdom 14 191 0.7× 77 0.3× 94 0.6× 39 0.3× 105 0.9× 23 532
Jorge Rozeira Portugal 9 695 2.5× 916 3.3× 219 1.4× 251 2.1× 83 0.7× 12 1.1k
Rajendra S. Sonawane India 11 136 0.5× 171 0.6× 72 0.5× 50 0.4× 112 1.0× 20 539
Jinman Kim Australia 7 270 1.0× 312 1.1× 124 0.8× 117 1.0× 109 1.0× 12 483
Nan Yang China 14 187 0.7× 78 0.3× 154 1.0× 35 0.3× 146 1.3× 54 538
Shuanglang Feng China 5 224 0.8× 99 0.4× 265 1.7× 46 0.4× 266 2.4× 9 544

Countries citing papers authored by Peng Tang

Since Specialization
Citations

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

Fields of papers citing papers by Peng Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peng Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Peng Tang. A scholar is included among the top collaborators of Peng Tang 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 Peng Tang. Peng Tang 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.
Tang, Peng, Xintong Yan, Nan Yang, et al.. (2024). Joint-individual fusion structure with fusion attention module for multi-modal skin cancer classification. Pattern Recognition. 154. 110604–110604. 9 indexed citations
2.
Wang, Shuping, et al.. (2024). Internal defect detection model based on laser ultrasonic signal decomposition and deep learning. Measurement. 242. 116194–116194. 4 indexed citations
3.
Tang, Peng, et al.. (2024). Prior and Prediction Inverse Kernel Transformer for Single Image Defocus Deblurring. Proceedings of the AAAI Conference on Artificial Intelligence. 38(6). 5145–5153. 2 indexed citations
4.
Li, Jinze & Peng Tang. (2023). Multisource Analysis of Big Data on Street Vitality Using GIS Mapping and Deep Learning: A Case Study of Ding Shu, China. Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia. 1. 565–574. 2 indexed citations
5.
Yang, Nan, Javier Del Ser, Peng Tang, et al.. (2023). Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation. IEEE Transactions on Neural Networks and Learning Systems. 35(6). 7391–7404. 26 indexed citations
6.
Xiang, Shao, Qiaokang Liang, & Peng Tang. (2023). Task-Oriented Compression Framework for Remote Sensing Satellite Data Transmission. IEEE Transactions on Industrial Informatics. 20(3). 3487–3496. 13 indexed citations
7.
Xiang, Shao, Mi Wang, Jing Xiao, et al.. (2023). Cloud Coverage Estimation Network for Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters. 20. 1–5. 5 indexed citations
8.
Krammer, Sebastian, et al.. (2022). Deep learning‐based classification of dermatological lesions given a limited amount of labelled data. Journal of the European Academy of Dermatology and Venereology. 36(12). 2516–2524. 4 indexed citations
9.
10.
Tang, Peng, Xintong Yan, Nan Yang, et al.. (2021). FusionM4Net: A multi-stage multi-modal learning algorithm for multi-label skin lesion classification. Medical Image Analysis. 76. 102307–102307. 77 indexed citations
11.
Tang, Peng, Xintong Yan, Qiaokang Liang, & Dan Zhang. (2021). AFLN-DGCL: Adaptive Feature Learning Network with Difficulty-Guided Curriculum Learning for skin lesion segmentation. Applied Soft Computing. 110. 107656–107656. 34 indexed citations
12.
Tang, Peng, et al.. (2021). Feature Pyramid Nonlocal Network With Transform Modal Ensemble Learning for Breast Tumor Segmentation in Ultrasound Images. IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control. 68(12). 3549–3559. 25 indexed citations
13.
Xiang, Shao, et al.. (2021). Dual-Pathway Change Detection Network Based on the Adaptive Fusion Module. IEEE Geoscience and Remote Sensing Letters. 19. 1–5. 5 indexed citations
14.
Xiang, Shao, et al.. (2021). Dual-Task Semantic Change Detection for Remote Sensing Images Using the Generative Change Field Module. Remote Sensing. 13(16). 3336–3336. 38 indexed citations
15.
Tang, Peng, Qiaokang Liang, Xintong Yan, Shao Xiang, & Dan Zhang. (2020). GP-CNN-DTEL: Global-Part CNN Model With Data-Transformed Ensemble Learning for Skin Lesion Classification. IEEE Journal of Biomedical and Health Informatics. 24(10). 2870–2882. 103 indexed citations
16.
Tang, Peng, Qiaokang Liang, Xintong Yan, et al.. (2019). Efficient skin lesion segmentation using separable-Unet with stochastic weight averaging. Computer Methods and Programs in Biomedicine. 178. 289–301. 145 indexed citations
17.
Xiang, Shao, et al.. (2019). AMC-Net: Asymmetric and multi-scale convolutional neural network for multi-label HPA classification. Computer Methods and Programs in Biomedicine. 178. 275–287. 8 indexed citations
18.
Zhang, Yi, Yan Zhou, Xinhua Yang, et al.. (2013). Thin slice three dimentional (3D) reconstruction versus CT 3D reconstruction of human breast cancer. SHILAP Revista de lepidopterología. 5 indexed citations
19.
Zhang, Yi, et al.. (2013). Thin slice three dimentional (3D) reconstruction versus CT 3D reconstruction of human breast cancer.. PubMed. 137(1). 57–62. 9 indexed citations
20.
Meng, Gang, Qiao‐Ying Yuan, Ling Zhong, et al.. (2013). Clinical Characteristics and Surgical Modality of Plasma Cell Mastitis: Analysis of 91 Cases. The American Surgeon. 79(1). 54–60. 27 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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