Kai Han

891 total citations · 1 hit paper
20 papers, 350 citations indexed

About

Kai Han is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Kai Han has authored 20 papers receiving a total of 350 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 10 papers in Artificial Intelligence and 9 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Kai Han's work include Advanced Neural Network Applications (10 papers), COVID-19 diagnosis using AI (6 papers) and Medical Image Segmentation Techniques (5 papers). Kai Han is often cited by papers focused on Advanced Neural Network Applications (10 papers), COVID-19 diagnosis using AI (6 papers) and Medical Image Segmentation Techniques (5 papers). Kai Han collaborates with scholars based in China, United States and Hong Kong. Kai Han's co-authors include Zhe Liu, Yuqing Song, Victor S. Sheng, Yang Lu, Kenneth K. Wong, Ying Shan, Yi Liu, Yan‐Pei Cao, Yan Zhu and Guanying Chen and has published in prestigious journals such as Expert Systems with Applications, IEEE Transactions on Medical Imaging and Knowledge-Based Systems.

In The Last Decade

Kai Han

19 papers receiving 341 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.

Deep semi-supervised learning for medical image segmentation: A review

2024 75 citations Kai Han, Victor S. Sheng et al. Expert Systems with Applications profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kai Han China	9	174	121	110	47	44	20	350
Irena Galić Croatia	12	197 1.1×	108 0.9×	139 1.3×	21 0.4×	64 1.5×	53	431
Min Dong China	7	139 0.8×	222 1.8×	160 1.5×	60 1.3×	26 0.6×	28	388
Zhuotun Zhu United States	9	424 2.4×	253 2.1×	266 2.4×	60 1.3×	68 1.5×	11	613
Fengze Liu United States	6	319 1.8×	148 1.2×	171 1.6×	40 0.9×	78 1.8×	10	473
Sibei Yang China	15	511 2.9×	354 2.9×	91 0.8×	28 0.6×	33 0.8×	26	714
E-Liang Chen Taiwan	6	282 1.6×	179 1.5×	96 0.9×	48 1.0×	38 0.9×	10	426
Delphine Nain United States	10	231 1.3×	45 0.4×	49 0.4×	11 0.2×	26 0.6×	16	359
Anfei Fan United States	3	585 3.4×	132 1.1×	160 1.5×	36 0.8×	105 2.4×	6	699
Tri Huynh United States	7	161 0.9×	147 1.2×	158 1.4×	13 0.3×	66 1.5×	11	458
Huan Xu China	3	372 2.1×	60 0.5×	56 0.5×	17 0.4×	30 0.7×	4	478

Countries citing papers authored by Kai Han

Since Specialization

Citations

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

Fields of papers citing papers by Kai Han

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kai Han

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

Han, Kai, et al.. (2025). Region Uncertainty Estimation for Medical Image Segmentation With Noisy Labels. IEEE Transactions on Medical Imaging. 44(12). 5197–5207.

Liu, Hao, et al.. (2025). Multispectral non-line-of-sight imaging via deep fusion photography. Science China Information Sciences. 68(4). 8 indexed citations

Lyu, C., Kai Han, Saisai Li, et al.. (2024). Automatic medical report generation combining contrastive learning and feature difference. Knowledge-Based Systems. 305. 112630–112630. 5 indexed citations

Han, Kai, et al.. (2024). Deep semi-supervised learning for medical image segmentation: A review. Expert Systems with Applications. 245. 123052–123052. 75 indexed citations breakdown →

Cao, Yan‐Pei, et al.. (2024). DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via Diffusion Models. 958–968. 39 indexed citations

Yin, Minghao, Shangzhe Wu, & Kai Han. (2024). IBD-SLAM: Learning Image-Based Depth Fusion for Generalizable SLAM. 10563–10573. 2 indexed citations

Song, Yuqing, et al.. (2023). MMMViT: Multiscale multimodal vision transformer for brain tumor segmentation with missing modalities. Biomedical Signal Processing and Control. 90. 105827–105827. 6 indexed citations

Zhong, Yujie, et al.. (2023). Open-Vocabulary Semantic Segmentation with Decoupled One-Pass Network. 1086–1096. 8 indexed citations

Zhu, Yan, Zhe Liu, Yuqing Song, et al.. (2023). CPSNet: a cyclic pyramid-based small lesion detection network. Multimedia Tools and Applications. 83(13). 39983–40001. 1 indexed citations

10.

Wang, Xiaoxiao, et al.. (2023). Noisy-to-Clean Label Learning for Medical Image Segmentation. 1553–1558. 1 indexed citations

11.

Rong, Huan, et al.. (2022). Cascaded multi-point regression Network for high-quality generic lesion detection. Expert Systems with Applications. 214. 119141–119141. 1 indexed citations

12.

Liu, Zhe, et al.. (2022). A survey on the interpretability of deep learning in medical diagnosis. Multimedia Systems. 28(6). 2335–2355. 92 indexed citations

13.

Hao, Wenfeng, Jing Zhang, Yuqing Song, et al.. (2022). HPM-Net: Hierarchical progressive multiscale network for liver vessel segmentation in CT images. Computer Methods and Programs in Biomedicine. 224. 107003–107003. 15 indexed citations

14.

Liu, Zhe, et al.. (2022). CMFCUNet: cascaded multi-scale feature calibration UNet for pancreas segmentation. Multimedia Systems. 29(2). 871–886. 15 indexed citations

15.

Liu, Zhe, et al.. (2022). Improving CT-image universal lesion detection with comprehensive data and feature enhancements. Multimedia Systems. 28(5). 1741–1752. 7 indexed citations

16.

Chen, Guanying, et al.. (2022). JIFF: Jointly-aligned Implicit Face Function for High Quality Single View Clothed Human Reconstruction. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2719–2729. 17 indexed citations

17.

Liu, Zhe, et al.. (2022). RTUNet: Residual transformer UNet specifically for pancreas segmentation. Biomedical Signal Processing and Control. 79. 104173–104173. 30 indexed citations

18.

Liu, Zhe, et al.. (2020). Pancreas Segmentation Based on an Optimized Coarse-to-Fine Method. 9413. 1–5. 3 indexed citations

19.

Liu, Zhe, Kai Han, Zhaohui Wang, et al.. (2020). Automatic liver segmentation from abdominal CT volumes using improved convolution neural networks. Multimedia Systems. 27(1). 111–124. 24 indexed citations

20.

Zhang, Zili, et al.. (2014). A Novel Arc Segmentation Approach for Document Image Processing. International Journal of Pattern Recognition and Artificial Intelligence. 29(1). 1553001–1553001. 1 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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