Shidan Wang

2.8k total citations · 1 hit paper
45 papers, 1.5k citations indexed

About

Shidan Wang is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Molecular Biology. According to data from OpenAlex, Shidan Wang has authored 45 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 15 papers in Radiology, Nuclear Medicine and Imaging and 12 papers in Molecular Biology. Recurrent topics in Shidan Wang's work include AI in cancer detection (14 papers), Radiomics and Machine Learning in Medical Imaging (12 papers) and Lung Cancer Diagnosis and Treatment (9 papers). Shidan Wang is often cited by papers focused on AI in cancer detection (14 papers), Radiomics and Machine Learning in Medical Imaging (12 papers) and Lung Cancer Diagnosis and Treatment (9 papers). Shidan Wang collaborates with scholars based in United States, China and Japan. Shidan Wang's co-authors include Guanghua Xiao, Yang Xie, Donghan M. Yang, Xiaowei Zhan, Ruichen Rong, Lin Yang, Guanghua Xiao, Junya Fujimoto, David E. Gerber and John D. Minna and has published in prestigious journals such as Journal of Clinical Investigation, Nature Communications and Gastroenterology.

In The Last Decade

Shidan Wang

45 papers receiving 1.5k 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.

A critical assessment of using ChatGPT for extracting structured data from clinical notes

2024 101 citations Donghan M. Yang, Ruichen Rong et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shidan Wang United States	20	559	550	376	337	313	45	1.5k
Mohamed Amgad United States	18	692 1.2×	717 1.3×	156 0.4×	195 0.6×	250 0.8×	32	1.6k
Catarina Eloy Portugal	24	863 1.5×	673 1.2×	261 0.7×	413 1.2×	417 1.3×	109	2.3k
Sebastian Foersch Germany	25	478 0.9×	568 1.0×	386 1.0×	767 2.3×	498 1.6×	70	2.2k
Lucian Beer Austria	23	293 0.5×	767 1.4×	333 0.9×	193 0.6×	357 1.1×	82	1.9k
Zhenwei Shi China	21	386 0.7×	815 1.5×	293 0.8×	213 0.6×	395 1.3×	66	1.5k
Liangping Li China	12	413 0.7×	714 1.3×	619 1.6×	1.1k 3.2×	217 0.7×	22	1.7k
Longzhong Liu China	20	414 0.7×	506 0.9×	145 0.4×	178 0.5×	236 0.8×	46	1.3k
Shigao Huang China	16	308 0.6×	455 0.8×	237 0.6×	193 0.6×	182 0.6×	46	1.2k
Shijun Wang United States	19	499 0.9×	790 1.4×	451 1.2×	149 0.4×	192 0.6×	65	1.8k
Georgios Z. Papadakis United States	20	252 0.5×	582 1.1×	347 0.9×	312 0.9×	124 0.4×	83	1.6k

Countries citing papers authored by Shidan Wang

Since Specialization

Citations

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

Fields of papers citing papers by Shidan Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shidan Wang

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

Rong, Ruichen, et al.. (2025). Image-based inference of tumor cell trajectories enables large-scale cancer progression analysis. Science Advances. 11(29). eadv9466–eadv9466. 1 indexed citations

Zhu, Shijia, Naoto Kubota, Shidan Wang, et al.. (2024). STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics. Nature Communications. 15(1). 7559–7559. 4 indexed citations

Wang, Shidan, Ruichen Rong, Zhuo Zhao, et al.. (2024). Cell Segmentation With Globally Optimized Boundaries (CSGO): A Deep Learning Pipeline for Whole-Cell Segmentation in Hematoxylin-and-Eosin–Stained Tissues. Laboratory Investigation. 105(2). 102184–102184. 4 indexed citations

Rong, Ruichen, Shidan Wang, Xinyi Zhang, et al.. (2023). Enhanced Pathology Image Quality with Restore–Generative Adversarial Network. American Journal Of Pathology. 193(4). 404–416. 13 indexed citations

Zhang, Xinyi, Frederico O. Gleber‐Netto, Shidan Wang, et al.. (2023). Deep learning‐based pathology image analysis predicts cancer progression risk in patients with oral leukoplakia. Cancer Medicine. 12(6). 7508–7518. 19 indexed citations

Wen, Zhuoyu, Yu-Hsuan Lin, Shidan Wang, et al.. (2023). Deep-Learning-Based Hepatic Ploidy Quantification Using H&E Histopathology Images. Genes. 14(4). 921–921. 4 indexed citations

Wang, Shidan, Ruichen Rong, Donghan M. Yang, et al.. (2023). Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images. Nature Communications. 14(1). 7872–7872. 20 indexed citations

Rong, Ruichen, Danni Luo, Zhuoyu Wen, et al.. (2023). A Deep Learning Approach for Histology-Based Nucleus Segmentation and Tumor Microenvironment Characterization. Modern Pathology. 36(8). 100196–100196. 33 indexed citations

Zhang, Xinyi, Frederico O. Gleber‐Netto, Shidan Wang, et al.. (2023). A Deep Learning Onion Peeling Approach to Measure Oral Epithelium Layer Number. Cancers. 15(15). 3891–3891. 3 indexed citations

10.

Lin, Han, et al.. (2023). Depression prediction based on LassoNet-RNN model: A longitudinal study. Heliyon. 9(10). e20684–e20684. 10 indexed citations

11.

Zhang, Xinyi, Shidan Wang, Erin R. Rudzinski, et al.. (2022). Deep Learning of Rhabdomyosarcoma Pathology Images for Classification and Survival Outcome Prediction. American Journal Of Pathology. 192(6). 917–925. 20 indexed citations

12.

Wang, Yunguan, Bing Song, Shidan Wang, et al.. (2022). Sprod for de-noising spatially resolved transcriptomics data based on position and image information. Nature Methods. 19(8). 950–958. 35 indexed citations

13.

Xu, Chunyan, et al.. (2021). Recent advances in understanding the roles of sialyltransferases in tumor angiogenesis and metastasis. Glycoconjugate Journal. 38(1). 119–127. 15 indexed citations

14.

Lu, Tianshi, Shidan Wang, Lin Xu, et al.. (2020). Tumor neoantigenicity assessment with CSiN score incorporates clonality and immunogenicity to predict immunotherapy outcomes. Science Immunology. 5(44). 30 indexed citations

15.

Lin, Yu-Hsuan, Shuyuan Zhang, Min Zhu, et al.. (2020). Mice With Increased Numbers of Polyploid Hepatocytes Maintain Regenerative Capacity But Develop Fewer Hepatocellular Carcinomas Following Chronic Liver Injury. Gastroenterology. 158(6). 1698–1712.e14. 64 indexed citations

16.

Wang, Shidan, Donghan M. Yang, Ruichen Rong, Xiaowei Zhan, & Guanghua Xiao. (2019). Pathology Image Analysis Using Segmentation Deep Learning Algorithms. American Journal Of Pathology. 189(9). 1686–1698. 244 indexed citations

17.

Wang, Shidan, Tao Wang, Lin Yang, et al.. (2019). ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network. EBioMedicine. 50. 103–110. 72 indexed citations

18.

Xie, Yang, Wei Lü, Shidan Wang, et al.. (2018). Validation of the 12-gene Predictive Signature for Adjuvant Chemotherapy Response in Lung Cancer. Clinical Cancer Research. 25(1). 150–157. 13 indexed citations

19.

Yang, Lin, Shidan Wang, David E. Gerber, et al.. (2018). Main bronchus location is a predictor for metastasis and prognosis in lung adenocarcinoma: A large cohort analysis. Lung Cancer. 120. 22–26. 18 indexed citations

20.

Yi, Faliu, Lin Yang, Shidan Wang, et al.. (2018). Microvessel prediction in H&E Stained Pathology Images using fully convolutional neural networks. BMC Bioinformatics. 19(1). 64–64. 38 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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