Tongtong Che

572 total citations
22 papers, 313 citations indexed

About

Tongtong Che is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Biomedical Engineering. According to data from OpenAlex, Tongtong Che has authored 22 papers receiving a total of 313 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Radiology, Nuclear Medicine and Imaging, 9 papers in Computer Vision and Pattern Recognition and 4 papers in Biomedical Engineering. Recurrent topics in Tongtong Che's work include Radiomics and Machine Learning in Medical Imaging (6 papers), Medical Imaging Techniques and Applications (5 papers) and Medical Image Segmentation Techniques (5 papers). Tongtong Che is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (6 papers), Medical Imaging Techniques and Applications (5 papers) and Medical Image Segmentation Techniques (5 papers). Tongtong Che collaborates with scholars based in China, Australia and Netherlands. Tongtong Che's co-authors include Shuyu Li, Xiuying Wang, Guoqing Bao, Yan Zhao, Yuanjie Zheng, Kun Zhao, Qiongling Li, Yong Liu, Yan Zhao and Yanhui Ding and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Transactions on Image Processing.

In The Last Decade

Tongtong Che

18 papers receiving 306 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tongtong Che China 11 146 68 67 66 45 22 313
D. Rodríguez United Kingdom 11 157 1.1× 73 1.1× 53 0.8× 109 1.7× 28 0.6× 23 367
Inas A. Yassine Egypt 9 150 1.0× 63 0.9× 77 1.1× 143 2.2× 46 1.0× 33 394
Xiang Fan China 8 247 1.7× 33 0.5× 99 1.5× 77 1.2× 46 1.0× 26 396
Benedikt A. Jónsson Iceland 4 93 0.6× 63 0.9× 32 0.5× 103 1.6× 48 1.1× 7 272
Tzu-An Song United States 10 190 1.3× 60 0.9× 115 1.7× 31 0.5× 25 0.6× 17 363
Samadrita Roy Chowdhury United States 8 127 0.9× 38 0.6× 94 1.4× 42 0.6× 28 0.6× 13 266
Alberto F. Goldszal United States 8 156 1.1× 60 0.9× 123 1.8× 74 1.1× 59 1.3× 14 385
Leon Y. Cai United States 13 264 1.8× 31 0.5× 49 0.7× 170 2.6× 72 1.6× 41 453
Yonggui Yang China 7 111 0.8× 111 1.6× 61 0.9× 56 0.8× 103 2.3× 16 355
April Khademi Canada 14 232 1.6× 134 2.0× 173 2.6× 60 0.9× 61 1.4× 48 525

Countries citing papers authored by Tongtong Che

Since Specialization
Citations

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

Fields of papers citing papers by Tongtong Che

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tongtong Che

This figure shows the co-authorship network connecting the top 25 collaborators of Tongtong Che. A scholar is included among the top collaborators of Tongtong Che 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 Tongtong Che. Tongtong Che 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.
Chu, Lei, et al.. (2025). A paired dataset of multi-modal MRI at 3 Tesla and 7 Tesla with manual hippocampal subfield segmentations. Scientific Data. 12(1). 260–260. 2 indexed citations
2.
3.
Shao, Yingchun, Qun Fang, Yansong Li, et al.. (2025). Mechanisms of Exercise in Improving Sarcopenia. Comprehensive physiology. 15(4). e70030–e70030. 2 indexed citations
4.
5.
Jiang, Xiaoxing & Tongtong Che. (2025). Effects of aerobic exercise combined with blood flow restriction on physical fitness and mental health of high school students. Frontiers in Psychology. 16. 1654855–1654855.
6.
Zhao, Yan, et al.. (2023). Graph neural networks for image‐guided disease diagnosis: A review. SHILAP Revista de lepidopterología. 1(2). 151–166. 15 indexed citations
7.
Che, Tongtong, Xiuying Wang, Kun Zhao, et al.. (2023). AMNet: Adaptive multi-level network for deformable registration of 3D brain MR images. Medical Image Analysis. 85. 102740–102740. 20 indexed citations
8.
Li, Xinwei, Hong Liu, Xiaoxi Dong, et al.. (2023). Syn_SegNet: A Joint Deep Neural Network for Ultrahigh-Field 7T MRI Synthesis and Hippocampal Subfield Segmentation in Routine 3T MRI. IEEE Journal of Biomedical and Health Informatics. 27(10). 4866–4877. 3 indexed citations
9.
Zheng, Yuanjie, Tongtong Che, Sujuan Hou, et al.. (2022). Image Matting With Deep Gaussian Process. IEEE Transactions on Neural Networks and Learning Systems. 34(11). 8879–8893. 13 indexed citations
10.
Zhao, Kun, Jiaji Lin, Martin Dyrba, et al.. (2022). Coupling of the spatial distributions between sMRI and PET reveals the progression of Alzheimer’s disease. Network Neuroscience. 7(1). 86–101. 5 indexed citations
11.
Zhao, Yan, Xiuying Wang, Tongtong Che, Guoqing Bao, & Shuyu Li. (2022). Multi-task deep learning for medical image computing and analysis: A review. Computers in Biology and Medicine. 153. 106496–106496. 84 indexed citations
12.
Zhao, Kun, et al.. (2022). A Hybrid Deep Learning Method for Early and Late Mild Cognitive Impairment Diagnosis With Incomplete Multimodal Data. Frontiers in Neuroinformatics. 16. 843566–843566. 12 indexed citations
13.
Zhao, Kun, Qiang Zheng, Martin Dyrba, et al.. (2022). Regional Radiomics Similarity Networks Reveal Distinct Subtypes and Abnormality Patterns in Mild Cognitive Impairment (Adv. Sci. 12/2022). Advanced Science. 9(12). 4 indexed citations
14.
Zhao, Kun, Qiang Zheng, Martin Dyrba, et al.. (2022). Regional Radiomics Similarity Networks Reveal Distinct Subtypes and Abnormality Patterns in Mild Cognitive Impairment. Advanced Science. 9(12). e2104538–e2104538. 41 indexed citations
15.
Zhao, Kun, Qiang Zheng, Tongtong Che, et al.. (2021). Regional radiomics similarity networks (R2SNs) in the human brain: Reproducibility, small-world properties and a biological basis. Network Neuroscience. 5(3). 1–15. 40 indexed citations
16.
Ding, Yanhui, Kun Zhao, Tongtong Che, et al.. (2021). Quantitative Radiomic Features as New Biomarkers for Alzheimer’s Disease: An Amyloid PET Study. Cerebral Cortex. 31(8). 3950–3961. 23 indexed citations
17.
Zhao, Yan, et al.. (2021). Multi-view prediction of Alzheimer’s disease progression with end-to-end integrated framework. Journal of Biomedical Informatics. 125. 103978–103978. 15 indexed citations
18.
Li, Shuo, et al.. (2020). Screening for Glaucoma from Fundus Images via Multitask Deep Learning. Investigative Ophthalmology & Visual Science. 61(7). 4530–4530. 1 indexed citations
19.
Zheng, Yuanjie, et al.. (2020). Joint Deep Matching Model of OCT Retinal Layer Segmentation. Computers, materials & continua/Computers, materials & continua (Print). 63(3). 1485–1498. 1 indexed citations
20.
Che, Tongtong, Yuanjie Zheng, Jinyu Cong, et al.. (2019). Deep Group-Wise Registration for Multi-Spectral Images From Fundus Images. IEEE Access. 7. 27650–27661. 17 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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