Changfa Shi

425 total citations
15 papers, 298 citations indexed

About

Changfa Shi is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Biomedical Engineering. According to data from OpenAlex, Changfa Shi has authored 15 papers receiving a total of 298 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 8 papers in Radiology, Nuclear Medicine and Imaging and 5 papers in Biomedical Engineering. Recurrent topics in Changfa Shi's work include Medical Image Segmentation Techniques (9 papers), Advanced Neural Network Applications (6 papers) and Medical Imaging and Analysis (5 papers). Changfa Shi is often cited by papers focused on Medical Image Segmentation Techniques (9 papers), Advanced Neural Network Applications (6 papers) and Medical Imaging and Analysis (5 papers). Changfa Shi collaborates with scholars based in China, Japan and United States. Changfa Shi's co-authors include Jinke Wang, Haiying Wang, Shinichi Tamura, Yuanzhi Cheng, Yadong Wang, Kensaku Mori, Fei Liu, Jing Bai, Xiangyang Zhang and Min Xian and has published in prestigious journals such as Scientific Reports, Pattern Recognition and Sustainability.

In The Last Decade

Changfa Shi

15 papers receiving 291 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Changfa Shi China 8 157 145 93 56 45 15 298
Lisa Di Jorio Canada 5 133 0.8× 163 1.1× 102 1.1× 75 1.3× 43 1.0× 9 319
Hui Cui China 3 199 1.3× 164 1.1× 119 1.3× 45 0.8× 73 1.6× 7 335
Weili Shi China 9 132 0.8× 101 0.7× 66 0.7× 55 1.0× 32 0.7× 64 297
Hongchun Lu China 8 169 1.1× 98 0.7× 123 1.3× 38 0.7× 52 1.2× 13 307
Lingjiao Pan China 10 172 1.1× 149 1.0× 51 0.5× 68 1.2× 31 0.7× 27 313
Matthew C. H. Lee United Kingdom 2 167 1.1× 107 0.7× 109 1.2× 39 0.7× 19 0.4× 2 288
Gabriel Efrain Humpire Mamani Netherlands 5 141 0.9× 86 0.6× 78 0.8× 47 0.8× 21 0.5× 6 282
Kejuan Yue China 7 203 1.3× 241 1.7× 87 0.9× 38 0.7× 38 0.8× 17 400
Mehrdad Moghbel Malaysia 10 196 1.2× 298 2.1× 167 1.8× 54 1.0× 53 1.2× 17 470

Countries citing papers authored by Changfa Shi

Since Specialization
Citations

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

Fields of papers citing papers by Changfa Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Changfa Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Changfa Shi. A scholar is included among the top collaborators of Changfa Shi 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 Changfa Shi. Changfa Shi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

15 of 15 papers shown
1.
Vakanski, Aleksandar, et al.. (2024). Bend-Net: Bending Loss Regularized Multitask Learning Network for Nuclei Segmentation in Histopathology Images. Information. 15(7). 417–417. 1 indexed citations
2.
3.
Wang, Jinke, et al.. (2023). 2.5D cascaded context-based network for liver and tumor segmentation from CT images. Electronic Research Archive. 31(8). 4324–4345. 5 indexed citations
4.
Wang, Jinke, et al.. (2023). MIC-Net: multi-scale integrated context network for automatic retinal vessel segmentation in fundus image. Mathematical Biosciences & Engineering. 20(4). 6912–6931. 1 indexed citations
5.
Wang, Jinke, et al.. (2022). Deep supervision and atrous inception-based U-Net combining CRF for automatic liver segmentation from CT. Scientific Reports. 12(1). 16995–16995. 11 indexed citations
6.
Wang, Jinke, et al.. (2022). Multi-scale attention and deep supervision-based 3D UNet for automatic liver segmentation from CT. Mathematical Biosciences & Engineering. 20(1). 1297–1316. 10 indexed citations
7.
Wang, Jinke, et al.. (2021). SAR-U-Net: squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver CT segmentation.. arXiv (Cornell University). 1 indexed citations
8.
Wang, Jinke, et al.. (2021). SAR-U-Net: Squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography. Computer Methods and Programs in Biomedicine. 208. 106268–106268. 107 indexed citations
9.
Shi, Changfa, et al.. (2021). Multi-slice low-rank tensor decomposition based multi-atlas segmentation: Application to automatic pathological liver CT segmentation. Medical Image Analysis. 73. 102152–102152. 13 indexed citations
10.
Wang, Jinke, et al.. (2021). SERR-U-Net: Squeeze-and-Excitation Residual and Recurrent Block-Based U-Net for Automatic Vessel Segmentation in Retinal Image. Computational and Mathematical Methods in Medicine. 2021. 1–16. 10 indexed citations
11.
Wang, Jinke, et al.. (2019). A Two-Stage High-Dimensional Feature Selection Method for Pulmonary Tumor Classification in CT. Journal of Medical Imaging and Health Informatics. 9(7). 1516–1523. 1 indexed citations
12.
Wang, Jinke & Changfa Shi. (2017). Automatic construction of statistical shape models using deformable simplex meshes with vector field convolution energy. BioMedical Engineering OnLine. 16(1). 49–49. 18 indexed citations
13.
Shi, Changfa, Yuanzhi Cheng, Jinke Wang, et al.. (2017). Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation. Medical Image Analysis. 38. 30–49. 60 indexed citations
14.
Shi, Changfa, Yuanzhi Cheng, Fei Liu, et al.. (2015). A hierarchical local region-based sparse shape composition for liver segmentation in CT scans. Pattern Recognition. 50. 88–106. 56 indexed citations
15.
Shi, Changfa, et al.. (2014). Greedy algorithm based deformable simplex meshes using gradient vector flow as external energy. 71. 199–204. 2 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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