Shaoting Zhang

15.6k total citations · 5 hit papers
249 papers, 6.8k citations indexed

About

Shaoting Zhang is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Shaoting Zhang has authored 249 papers receiving a total of 6.8k indexed citations (citations by other indexed papers that have themselves been cited), including 139 papers in Computer Vision and Pattern Recognition, 90 papers in Radiology, Nuclear Medicine and Imaging and 83 papers in Artificial Intelligence. Recurrent topics in Shaoting Zhang's work include Medical Image Segmentation Techniques (64 papers), AI in cancer detection (54 papers) and Radiomics and Machine Learning in Medical Imaging (46 papers). Shaoting Zhang is often cited by papers focused on Medical Image Segmentation Techniques (64 papers), AI in cancer detection (54 papers) and Radiomics and Machine Learning in Medical Imaging (46 papers). Shaoting Zhang collaborates with scholars based in United States, China and Hong Kong. Shaoting Zhang's co-authors include Dimitris Metaxas, Dimitris Metaxas, Junzhou Huang, Xiaofan Zhang, Guotai Wang, Kang Li, Yiqiang Zhan, Hongsheng Li, Wei Liu and Shu Wang and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Shaoting Zhang

231 papers receiving 6.6k citations

Hit Papers

5 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.

Multispectral Deep Neural Networks for Pedestrian Detection

2016 340 citations Shaoting Zhang, Dimitris Metaxas et al. profile →
TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers

2024 287 citations Yongyi Lu, Xiangde Luo et al. Medical Image Analysis profile →
Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency

2022 194 citations Xiangde Luo, Guotai Wang et al. Medical Image Analysis profile →
On the challenges and perspectives of foundation models for medical image analysis

2023 105 citations Shaoting Zhang, Dimitris Metaxas Medical Image Analysis profile →
A Comprehensive Survey of Foundation Models in Medicine

2025 16 citations Shaoting Zhang et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shaoting Zhang United States	39	3.9k	2.2k	2.1k	958	477	249	6.8k
Ghassan Hamarneh Canada	39	2.7k 0.7×	1.8k 0.8×	1.9k 0.9×	1.1k 1.1×	250 0.5×	252	6.7k
Hayit Greenspan Israel	40	4.6k 1.2×	2.5k 1.1×	2.3k 1.1×	713 0.7×	988 2.1×	182	8.3k
Weidong Cai Australia	38	3.0k 0.8×	2.4k 1.1×	2.1k 1.0×	522 0.5×	498 1.0×	303	7.0k
Isabelle Bloch France	39	3.0k 0.8×	1.5k 0.7×	1.6k 0.8×	739 0.8×	699 1.5×	319	7.0k
Qian Wang China	42	2.4k 0.6×	1.4k 0.6×	3.2k 1.5×	1.2k 1.3×	279 0.6×	308	7.0k
Yizhou Yu China	51	6.2k 1.6×	2.3k 1.0×	1.5k 0.7×	464 0.5×	721 1.5×	221	9.5k
Yefeng Zheng China	44	3.1k 0.8×	2.2k 1.0×	2.1k 1.0×	1.2k 1.2×	440 0.9×	293	6.8k
Dimitris Metaxas United States	40	3.3k 0.8×	1.4k 0.6×	1.5k 0.7×	906 0.9×	304 0.6×	181	6.0k
Gustavo Carneiro Australia	40	3.3k 0.8×	2.9k 1.3×	1.9k 0.9×	658 0.7×	433 0.9×	179	6.6k
Ben Glocker United Kingdom	40	5.0k 1.3×	2.2k 1.0×	3.4k 1.6×	1.5k 1.6×	252 0.5×	151	10.1k

Countries citing papers authored by Shaoting Zhang

Since Specialization

Citations

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

Fields of papers citing papers by Shaoting Zhang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shaoting Zhang

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

Deng, Zhongying, Junlong Cheng, Yanzhou Su, et al.. (2025). SAM-Med3D: A Vision Foundation Model for General-Purpose Segmentation on Volumetric Medical Images. IEEE Transactions on Neural Networks and Learning Systems. 36(10). 17599–17612. 1 indexed citations

Liao, Xin, et al.. (2025). UniSAL: Unified Semi-supervised Active Learning for histopathological image classification. Medical Image Analysis. 102. 103542–103542.

Zhu, Wentao, Yuan Jin, Geng Chen, et al.. (2024). Classification of lung cancer subtypes on CT images with synthetic pathological priors. Medical Image Analysis. 95. 103199–103199. 6 indexed citations

Shi, Xiaoming, Zeming Liu, Li Du, et al.. (2024). Medical Dialogue System: A Survey of Categories, Methods, Evaluation and Challenges. 2840–2861. 3 indexed citations

Huang, Weijian, Cheng Li, Hong-Yu Zhou, et al.. (2024). Enhancing representation in radiography-reports foundation model: a granular alignment algorithm using masked contrastive learning. Nature Communications. 15(1). 7620–7620. 18 indexed citations

Yan, Fang, Qian Da, Hongmei Yi, et al.. (2024). Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma. npj Precision Oncology. 8(1). 76–76. 7 indexed citations

Wang, Xiaosong, Yongyi Lu, Yuyin Zhou, et al.. (2024). Boosting Dermatoscopic Lesion Segmentation Via Diffusion Models With Visual And Textual Prompts. 1–5. 3 indexed citations

Gu, Ran, Wenjun Liao, Shichuan Zhang, et al.. (2024). Domain composition and attention network trained with synthesized unlabeled images for generalizable medical image segmentation. Neurocomputing. 599. 128115–128115.

Chen, Xi, Wang Li, Mingke You, et al.. (2024). Evaluating and Enhancing Large Language Models’ Performance in Domain-Specific Medicine: Development and Usability Study With DocOA. Journal of Medical Internet Research. 26. e58158–e58158. 13 indexed citations

10.

Zuo, Shu-Qing, et al.. (2024). Evolution of the “Internet Plus Health Care” Mode Enabled by Artificial Intelligence: Development and Application of an Outpatient Triage System. Journal of Medical Internet Research. 26. e51711–e51711. 2 indexed citations

11.

Wang, Xiaosong, Qian Da, Xiaoqiang Liu, et al.. (2023). A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification. Scientific Data. 10(1). 574–574. 30 indexed citations

12.

Lei, Wenhui, Qi Su, Ran Gu, et al.. (2023). One-Shot Weakly-Supervised Segmentation in 3D Medical Images. IEEE Transactions on Medical Imaging. 43(1). 175–189. 18 indexed citations

13.

Ye, Yuxiang, Yechong Huang, Xin Dou, et al.. (2023). A novel one-to-multiple unsupervised domain adaptation framework for abdominal organ segmentation. Medical Image Analysis. 88. 102873–102873. 13 indexed citations

14.

Wang, Guotai, et al.. (2023). HAMIL: High-Resolution Activation Maps and Interleaved Learning for Weakly Supervised Segmentation of Histopathological Images. IEEE Transactions on Medical Imaging. 42(10). 2912–2923. 12 indexed citations

15.

Liu, Qiao, Wanwen Zeng, Wei Zhang, et al.. (2022). Deep generative modeling and clustering of single cell Hi-C data. Briefings in Bioinformatics. 24(1). 15 indexed citations

16.

Wang, Guotai, et al.. (2022). HMRNet: High and Multi-Resolution Network With Bidirectional Feature Calibration for Brain Structure Segmentation in Radiotherapy. IEEE Journal of Biomedical and Health Informatics. 26(9). 4519–4529. 17 indexed citations

17.

Lao, Qicheng, et al.. (2022). Thyroid nodule segmentation and classification in ultrasound images through intra- and inter-task consistent learning. Medical Image Analysis. 79. 102443–102443. 43 indexed citations

18.

Wang, Guotai, Hui Sun, Xiangde Luo, et al.. (2022). Learning COVID-19 Pneumonia Lesion Segmentation From Imperfect Annotations via Divergence-Aware Selective Training. IEEE Journal of Biomedical and Health Informatics. 26(8). 3673–3684. 10 indexed citations

19.

Luo, Xiangde, Guotai Wang, Tao Song, et al.. (2021). MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning. Medical Image Analysis. 72. 102102–102102. 75 indexed citations

20.

Zhang, Han, Tao Xu, Mohamed Elhoseiny, et al.. (2016). SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-Grained Recognition. 1143–1152. 206 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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