Chu Han

2.4k total citations · 2 hit papers
69 papers, 1.4k citations indexed

About

Chu Han is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Chu Han has authored 69 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Computer Vision and Pattern Recognition, 34 papers in Radiology, Nuclear Medicine and Imaging and 30 papers in Artificial Intelligence. Recurrent topics in Chu Han's work include Radiomics and Machine Learning in Medical Imaging (32 papers), AI in cancer detection (27 papers) and Digital Imaging for Blood Diseases (10 papers). Chu Han is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (32 papers), AI in cancer detection (27 papers) and Digital Imaging for Blood Diseases (10 papers). Chu Han collaborates with scholars based in China, Hong Kong and Netherlands. Chu Han's co-authors include Zaiyi Liu, Guoqiang Han, Xipeng Pan, Zhenwei Shi, Jing Qin, Zeyan Xu, Shengfeng He, Changhong Liang, Bingchao Zhao and Huan Lin and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Radiology and Expert Systems with Applications.

In The Last Decade

Chu Han

62 papers receiving 1.4k 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.

MRI-based Quantification of Intratumoral Heterogeneity for Predicting Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer

2023 112 citations Zhenwei Shi, Xiaomei Huang et al. profile →
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer With Modality-Correlated Cross-Attention for Brain Tumor Segmentation

2023 103 citations Jiatai Lin, Hao Chen et al. IEEE Transactions on Medical Imaging profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chu Han China	21	637	563	543	133	111	69	1.4k
Kumar Abhishek India	14	404 0.6×	362 0.6×	251 0.5×	81 0.6×	41 0.4×	49	1.2k
Dong Zhao China	18	495 0.8×	906 1.6×	239 0.4×	66 0.5×	56 0.5×	50	1.9k
Cheng Chen China	22	662 1.0×	949 1.7×	728 1.3×	166 1.2×	106 1.0×	87	2.1k
Chuang Zhu China	17	789 1.2×	430 0.8×	276 0.5×	58 0.4×	35 0.3×	105	1.5k
Idit Diamant Israel	11	686 1.1×	868 1.5×	752 1.4×	144 1.1×	152 1.4×	19	1.9k
Jie Du China	17	315 0.5×	469 0.8×	221 0.4×	100 0.8×	51 0.5×	42	957
Zhenbing Liu China	24	961 1.5×	688 1.2×	300 0.6×	195 1.5×	45 0.4×	101	1.8k
N. Sri Madhava Raja India	16	488 0.8×	464 0.8×	543 1.0×	239 1.8×	167 1.5×	46	1.3k
Marwa M. Emam Egypt	18	387 0.6×	860 1.5×	309 0.6×	157 1.2×	35 0.3×	35	1.4k
Mohammed Elmogy Egypt	26	817 1.3×	768 1.4×	910 1.7×	397 3.0×	160 1.4×	169	2.5k

Countries citing papers authored by Chu Han

Since Specialization

Citations

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

Fields of papers citing papers by Chu Han

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chu Han

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

Lin, Jiatai, Hao Wang, Danyi Li, et al.. (2025). Rethinking mitosis detection: Towards diverse data and feature representation for better domain generalization. Artificial Intelligence in Medicine. 163. 103097–103097.

Xu, Zeyan, et al.. (2025). Bounding boxes for weakly-supervised breast cancer segmentation in DCE-MRI. Biomedical Signal Processing and Control. 105. 107656–107656. 3 indexed citations

Li, Weixing, Zhen Zhang, Guangyao Wu, et al.. (2025). Data-efficient federated semi-supervised learning framework via pseudo supervision refinement strategy for lung tumor segmentation. Biomedical Signal Processing and Control. 107. 107793–107793.

Huang, Yanqi, Zhihong Chen, Zhongqiu Lin, et al.. (2025). Multi-phase feature-aligned fusion model for automated colorectal cancer segmentation in contrast-enhanced CT scans. Expert Systems with Applications. 284. 127727–127727.

Zhou, Nan, Siyang Feng, Zhenbing Liu, et al.. (2025). Uncertainty-guided cross teaching semi-supervised framework for histopathology image segmentation with curriculum self-training. Applied Soft Computing. 180. 113328–113328.

Liu, Zhenbing, Yanfen Cui, Xipeng Pan, et al.. (2025). Label-efficient transformer-based framework with self-supervised strategies for heterogeneous lung tumor segmentation. Expert Systems with Applications. 269. 126364–126364. 1 indexed citations

Yang, Qi, Yi Dai, Zeyan Xu, et al.. (2025). AI-driven MRI biomarker for triple-class HER2 expression classification in breast cancer: a large-scale multicenter study. Breast Cancer Research. 27(1). 166–166.

Chen, Zhihong, Yanfen Cui, Suyun Li, et al.. (2024). ALIEN: Attention-guided cross-resolution collaborative network for 3D gastric cancer segmentation in CT images. Biomedical Signal Processing and Control. 96. 106500–106500. 9 indexed citations

Xu, Peng, Yuankui Wu, Chu Han, et al.. (2024). CroMAM: A Cross-Magnification Attention Feature Fusion Model for Predicting Genetic Status and Survival of Gliomas Using Histological Images. IEEE Journal of Biomedical and Health Informatics. 28(12). 7345–7356. 3 indexed citations

10.

Xia, Yingda, Zhihong Chen, Suyun Li, et al.. (2024). A Colorectal Coordinate-Driven Method for Colorectum and Colorectal Cancer Segmentation in Conventional CT Scans. IEEE Transactions on Neural Networks and Learning Systems. 36(4). 7395–7406. 4 indexed citations

11.

Huang, Yanqi, Guoqiang Han, Zhenwei Shi, et al.. (2024). FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification. IEEE Transactions on Cybernetics. 54(12). 7851–7864. 6 indexed citations

12.

Wang, Dan, Chu Han, Zhen Zhang, et al.. (2024). FedDUS: Lung tumor segmentation on CT images through federated semi-supervised with dynamic update strategy. Computer Methods and Programs in Biomedicine. 249. 108141–108141. 8 indexed citations

13.

Pan, Xipeng, Rushi Lan, Cheng Lu, et al.. (2023). SMILE: Cost-sensitive multi-task learning for nuclear segmentation and classification with imbalanced annotations. Medical Image Analysis. 88. 102867–102867. 37 indexed citations

14.

Xu, Zeyan, Yu Xie, Yanfen Cui, et al.. (2023). Joint-phase attention network for breast cancer segmentation in DCE-MRI. Expert Systems with Applications. 224. 119962–119962. 23 indexed citations

15.

Lin, Jiatai, Guoqiang Han, Xipeng Pan, et al.. (2022). PDBL: Improving Histopathological Tissue Classification With Plug-and-Play Pyramidal Deep-Broad Learning. IEEE Transactions on Medical Imaging. 41(9). 2252–2262. 39 indexed citations

16.

Luo, Luyang, Hao Chen, Yanning Zhou, et al.. (2022). Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning–based Radiograph Diagnosis: A Multicenter Study. Radiology Artificial Intelligence. 4(5). e210299–e210299. 18 indexed citations

17.

Liang, Yun, Baozhen Zeng, Chao Zhu, et al.. (2022). Preoperative prediction of intra-tumoral tertiary lymphoid structures based on CT in hepatocellular cancer. European Journal of Radiology. 151. 110309–110309. 17 indexed citations

18.

Han, Chu, Jiatai Lin, Yi Wang, et al.. (2022). Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels. Medical Image Analysis. 80. 102487–102487. 68 indexed citations

19.

Xu, Zeyan, Ke Zhao, Lujun Han, et al.. (2021). Combining quantitative and qualitative magnetic resonance imaging features to differentiate anorectal malignant melanoma from low rectal cancer. Precision Clinical Medicine. 4(2). 119–128. 1 indexed citations

20.

Xu, Zeyan, Yong Li, Yingyi Wang, et al.. (2021). A deep learning quantified stroma-immune score to predict survival of patients with stage II–III colorectal cancer. Cancer Cell International. 21(1). 585–585. 24 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.

Explore authors with similar magnitude of impact