Chao Han

907 total citations
38 papers, 681 citations indexed

About

Chao Han is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Computer Vision and Pattern Recognition. According to data from OpenAlex, Chao Han has authored 38 papers receiving a total of 681 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Radiology, Nuclear Medicine and Imaging, 13 papers in Pulmonary and Respiratory Medicine and 8 papers in Computer Vision and Pattern Recognition. Recurrent topics in Chao Han's work include Radiomics and Machine Learning in Medical Imaging (9 papers), Prostate Cancer Diagnosis and Treatment (7 papers) and Prostate Cancer Treatment and Research (6 papers). Chao Han is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (9 papers), Prostate Cancer Diagnosis and Treatment (7 papers) and Prostate Cancer Treatment and Research (6 papers). Chao Han collaborates with scholars based in China, United States and Malaysia. Chao Han's co-authors include Thomas S. Hatsukami, Chun Yuan, Nayak L. Polissar, Xiaoying Wang, Shuai Ma, Xiaodong Zhang, Yingpu Cui, Ying Luo, Huihui Xie and Xiang Liu and has published in prestigious journals such as Radiology, International Journal of Molecular Sciences and IEEE Transactions on Image Processing.

In The Last Decade

Chao Han

34 papers receiving 665 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chao Han China 16 430 380 148 95 78 38 681
Haojun Yu China 19 211 0.5× 837 2.2× 53 0.4× 102 1.1× 152 1.9× 68 1.2k
W. Kerwin United States 9 447 1.0× 316 0.8× 270 1.8× 33 0.3× 89 1.1× 15 607
Xiangfei Chai China 13 164 0.4× 269 0.7× 35 0.2× 76 0.8× 91 1.2× 19 542
Kyong Joon Lee South Korea 14 97 0.2× 217 0.6× 35 0.2× 75 0.8× 128 1.6× 40 575
Qi Peng United States 16 59 0.1× 301 0.8× 68 0.5× 54 0.6× 72 0.9× 51 648
Javier Oliván Bescós Netherlands 10 241 0.6× 129 0.3× 64 0.4× 101 1.1× 43 0.6× 24 625
Dániel Tóth United Kingdom 14 91 0.2× 160 0.4× 82 0.6× 156 1.6× 96 1.2× 49 573
Pieter Slagmolen Belgium 17 304 0.7× 501 1.3× 86 0.6× 231 2.4× 90 1.2× 36 970
Rubén Cárdenes Spain 15 238 0.6× 227 0.6× 165 1.1× 166 1.7× 208 2.7× 36 752
Themis P. Exarchos Greece 12 155 0.4× 190 0.5× 163 1.1× 26 0.3× 277 3.6× 41 563

Countries citing papers authored by Chao Han

Since Specialization
Citations

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

Fields of papers citing papers by Chao Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chao Han

This figure shows the co-authorship network connecting the top 25 collaborators of Chao Han. A scholar is included among the top collaborators of Chao Han 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 Chao Han. Chao Han 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.
Han, Chao, et al.. (2024). General numerical solution of integral equation for long‐range low‐frequency groundwave propagation over irregular terrain. IET Microwaves Antennas & Propagation. 18(12). 1148–1155.
2.
Shuang, Feng, et al.. (2023). Foodnet: multi-scale and label dependency learning-based multi-task network for food and ingredient recognition. Neural Computing and Applications. 36(9). 4485–4501. 2 indexed citations
3.
Li, Xiuhua, et al.. (2023). Fast Recognition and Counting Method of Dragon Fruit Flowers and Fruits Based on Video Stream. Sensors. 23(20). 8444–8444. 7 indexed citations
4.
Zhu, Li, Ge Gao, Yi Zhu, et al.. (2022). Fully automated detection and localization of clinically significant prostate cancer on MR images using a cascaded convolutional neural network. Frontiers in Oncology. 12. 958065–958065. 10 indexed citations
5.
Han, Chao, et al.. (2022). Fabric surface defect recognition based on multibranch residual network. Journal of Electronic Imaging. 31(6).
6.
Han, Chao, Shuai Ma, Xiang Liu, et al.. (2021). Radiomics Models Based on Apparent Diffusion Coefficient Maps for the Prediction of High‐Grade Prostate Cancer at Radical Prostatectomy: Comparison With Preoperative Biopsy. Journal of Magnetic Resonance Imaging. 54(6). 1892–1901. 26 indexed citations
7.
Liu, Xiang, Zhaonan Sun, Chao Han, et al.. (2021). Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images. BMC Medical Imaging. 21(1). 170–170. 15 indexed citations
8.
Liu, Xiang, Chao Han, He Wang, et al.. (2021). Fully automated pelvic bone segmentation in multiparameteric MRI using a 3D convolutional neural network. Insights into Imaging. 12(1). 93–93. 21 indexed citations
9.
Han, Chao, et al.. (2021). Differential diagnosis of uncommon prostate diseases: combining mpMRI and clinical information. Insights into Imaging. 12(1). 79–79. 20 indexed citations
10.
Liu, Xiang, Chao Han, Yingpu Cui, et al.. (2021). Detection and Segmentation of Pelvic Bones Metastases in MRI Images for Patients With Prostate Cancer Based on Deep Learning. Frontiers in Oncology. 11. 773299–773299. 23 indexed citations
11.
Han, Chao, et al.. (2020). A Novel Framework for Improving Pulse-Coupled Neural Networks With Fuzzy Connectedness for Medical Image Segmentation. IEEE Access. 8. 138129–138140. 8 indexed citations
12.
13.
14.
Ma, Shuai, Huihui Xie, Chao Han, et al.. (2019). MRI‐Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer. Journal of Magnetic Resonance Imaging. 50(6). 1914–1925. 54 indexed citations
15.
Zhu, Li, Ge Gao, Yi Liu, et al.. (2019). Feasibility of integrating computer-aided diagnosis with structured reports of prostate multiparametric MRI. Clinical Imaging. 60(1). 123–130. 21 indexed citations
16.
Ma, Shuai, et al.. (2019). Preoperative Prediction of Extracapsular Extension: Radiomics Signature Based on Magnetic Resonance Imaging to Stage Prostate Cancer. Molecular Imaging and Biology. 22(3). 711–721. 36 indexed citations
17.
Han, Chao, et al.. (2017). Efficient graph-based V2V free space fusion. TUbilio (Technical University of Darmstadt). 31. 985–992. 9 indexed citations
18.
Wang, Chengwei, et al.. (2015). Therapeutic effect of multiple burr hole operation combined with dural inversion and periosteal synangiosis for moyamoya disease. British Journal of Neurosurgery. 29(6). 811–817. 23 indexed citations
19.
Luo, Ying, Nayak L. Polissar, Chao Han, et al.. (2003). Accuracy and uniqueness of three in vivo measurements of atherosclerotic carotid plaque morphology with black blood MRI. Magnetic Resonance in Medicine. 50(1). 75–82. 48 indexed citations
20.
Polissar, Nayak L., et al.. (2000). Analysis of the measurement precision of arterial lumen and wall areas using high-resolution MRI. Magnetic Resonance in Medicine. 44(6). 968–972. 73 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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