Junzhou Huang

24.6k total citations · 11 hit papers
276 papers, 12.2k citations indexed

About

Junzhou Huang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Junzhou Huang has authored 276 papers receiving a total of 12.2k indexed citations (citations by other indexed papers that have themselves been cited), including 122 papers in Artificial Intelligence, 113 papers in Computer Vision and Pattern Recognition and 60 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Junzhou Huang's work include Sparse and Compressive Sensing Techniques (39 papers), AI in cancer detection (34 papers) and Computational Drug Discovery Methods (31 papers). Junzhou Huang is often cited by papers focused on Sparse and Compressive Sensing Techniques (39 papers), AI in cancer detection (34 papers) and Computational Drug Discovery Methods (31 papers). Junzhou Huang collaborates with scholars based in United States, China and Hong Kong. Junzhou Huang's co-authors include Yu Rong, Wenbing Huang, Tingyang Xu, Shaoting Zhang, Peilin Zhao, Dimitris Metaxas, Chen Chen, Tong Zhang, Dimitris Metaxas and Yeqing Li and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Junzhou Huang

271 papers receiving 12.0k citations

Hit Papers

Adaptive Graph Convolutional Neural Networks 2011 2026 2016 2021 2018 2015 2020 2011 2020 100 200 300 400
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.

  1. Adaptive Graph Convolutional Neural Networks
    2018 478 citations Junzhou Huang et al. profile →
  2. Large-Scale Multi-View Spectral Clustering via Bipartite Graph
    2015 461 citations Junzhou Huang et al. profile →
  3. Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks
    2020 435 citations Tian Bian, Tingyang Xu et al. profile →
  4. Robust tracking using local sparse appearance model and K-selection
    2011 355 citations Junzhou Huang et al. profile →
  5. Graph Representation Learning via Graphical Mutual Information Maximization
    2020 339 citations Wenbing Huang, Yu Rong et al. profile →
  6. Graph Convolutional Networks for Temporal Action Localization
    2019 335 citations Runhao Zeng, Wenbing Huang et al. profile →
  7. Progressive Feature Alignment for Unsupervised Domain Adaptation
    2019 299 citations Wenbing Huang, Yu Rong et al. profile →
  8. Transformer-based unsupervised contrastive learning for histopathological image classification
    2022 283 citations Junzhou Huang, Xiao Han et al. profile →
  9. Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks
    2020 264 citations Junzhou Huang et al. profile →
  10. scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data
    2022 244 citations Junzhou Huang, Jianhua Yao et al. profile →
  11. Vision-Language Pre-Training with Triple Contrastive Learning
    2022 170 citations Junzhou Huang et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Junzhou Huang United States 54 5.4k 5.2k 2.1k 1.3k 1.2k 276 12.2k
Yi Zhang China 59 4.2k 0.8× 4.6k 0.9× 2.3k 1.1× 438 0.3× 742 0.6× 570 12.2k
Kilian Q. Weinberger United States 54 8.4k 1.6× 8.3k 1.6× 726 0.3× 651 0.5× 722 0.6× 133 17.0k
Heng Huang United States 60 8.7k 1.6× 7.6k 1.5× 1.2k 0.6× 1.8k 1.4× 1.6k 1.4× 530 16.7k
Shuiwang Ji United States 43 3.1k 0.6× 3.5k 0.7× 784 0.4× 996 0.8× 637 0.5× 134 7.4k
Pascal Vincent Canada 26 5.6k 1.0× 6.8k 1.3× 645 0.3× 671 0.5× 621 0.5× 48 13.8k
Diederik P. Kingma United States 13 5.3k 1.0× 6.0k 1.2× 573 0.3× 549 0.4× 561 0.5× 18 12.1k
George E. Dahl United States 21 3.8k 0.7× 9.8k 1.9× 642 0.3× 1.1k 0.9× 486 0.4× 25 17.6k
Mark E. Shields United States 3 3.1k 0.6× 4.3k 0.8× 665 0.3× 981 0.7× 363 0.3× 6 10.7k
Joachim M. Buhmann Switzerland 54 4.6k 0.8× 3.5k 0.7× 961 0.5× 2.1k 1.6× 448 0.4× 235 12.5k
Yin Zhang⋆ China 63 2.4k 0.4× 4.1k 0.8× 985 0.5× 554 0.4× 492 0.4× 512 15.0k

Countries citing papers authored by Junzhou Huang

Since Specialization
Citations

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

Fields of papers citing papers by Junzhou Huang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Junzhou Huang

This figure shows the co-authorship network connecting the top 25 collaborators of Junzhou Huang. A scholar is included among the top collaborators of Junzhou Huang 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 Junzhou Huang. Junzhou Huang 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.
Zhang, Jiying, Fuyang Li, Xi Xiao, et al.. (2025). A Unified Random Walk, Its Induced Laplacians and Spectral Convolutions for Deep Hypergraph Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(11). 10129–10141.
2.
Yang, Han, Kangfei Zhao, Lanqing Li, et al.. (2024). Solving the non-submodular network collapse problems via Decision Transformer. Neural Networks. 176. 106328–106328.
3.
Bai, Qifeng, Tingyang Xu, Junzhou Huang, & Horacio Pérez‐Sánchez. (2024). Geometric deep learning methods and applications in 3D structure-based drug design. Drug Discovery Today. 29(7). 104024–104024. 15 indexed citations
4.
Zhao, Yu, Bing He, Zongbo Han, et al.. (2024). Learning With Noisy Labels Over Imbalanced Subpopulations. IEEE Transactions on Neural Networks and Learning Systems. 36(4). 6544–6555. 7 indexed citations
5.
Tan, Jie, Yu Rong, Kangfei Zhao, et al.. (2024). Natural Language-Assisted Multi-modal Medication Recommendation. arXiv (Cornell University). 2200–2209. 1 indexed citations
6.
Bian, Tian, Yifan Niu, Heng Chang, et al.. (2024). Hierarchical Graph Latent Diffusion Model for Conditional Molecule Generation. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 130–140. 2 indexed citations
7.
Han, Jiaqi, Wenbing Huang, Yu Rong, et al.. (2023). Structure-Aware DropEdge Toward Deep Graph Convolutional Networks. IEEE Transactions on Neural Networks and Learning Systems. 35(11). 15565–15577. 11 indexed citations
8.
Meng, Yue, Jun Zhang, Xinran Wang, et al.. (2021). Can AI-assisted microscope facilitate breast HER2 interpretation? A multi-institutional ring study. Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin. 479(3). 443–449. 33 indexed citations
9.
Chang, Heng, Yu Rong, Tingyang Xu, et al.. (2021). Not All Low-Pass Filters are Robust in Graph Convolutional Networks. Neural Information Processing Systems. 34. 14 indexed citations
10.
Wang, Xinran, Liang Wang, Hong Bu, et al.. (2021). How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies. npj Breast Cancer. 7(1). 61–61. 31 indexed citations
11.
Niu, Shuaicheng, Jiaxiang Wu, Yifan Zhang, et al.. (2021). Disturbance-immune weight sharing for neural architecture search. Neural Networks. 144. 553–564. 14 indexed citations
12.
Zeng, Runhao, Wenbing Huang, Mingkui Tan, et al.. (2021). Graph Convolutional Module for Temporal Action Localization in Videos. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(10). 6209–6223. 61 indexed citations
13.
Huang, Junzhou, et al.. (2020). Adversarial Domain Adaptation for Cell Segmentation. 277–287. 7 indexed citations
14.
Zhang, Yifan, Ying Wei, Qingyao Wu, et al.. (2020). Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis. IEEE Transactions on Image Processing. 29. 7834–7844. 113 indexed citations
15.
Zhang, Yifan, Peilin Zhao, Shuaicheng Niu, et al.. (2019). Online Adaptive Asymmetric Active Learning With Limited Budgets. IEEE Transactions on Knowledge and Data Engineering. 33(6). 2680–2692. 21 indexed citations
16.
Liu, Bo, et al.. (2019). Distributed Inexact Newton-type Pursuit for Non-convex Sparse Learning. International Conference on Artificial Intelligence and Statistics. 343–352. 3 indexed citations
17.
Wang, Xinggang, et al.. (2018). Deep Multi-instance Learning with Dynamic Pooling. Asian Conference on Machine Learning. 662–677. 19 indexed citations
18.
Deng, Cheng, et al.. (2015). Multi-view matrix decomposition: a new scheme for exploring discriminative information. International Conference on Artificial Intelligence. 3438–3444. 23 indexed citations
19.
Fang, Ruogu, et al.. (2015). Tissue-specific sparse deconvolution for brain CT perfusion. Computerized Medical Imaging and Graphics. 46. 64–72. 4 indexed citations
20.
Huang, Junzhou & Fei Yang. (2012). Compressed magnetic resonance imaging based on wavelet sparsity and nonlocal total variation. 968–971. 44 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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