Shu Liao

3.8k total citations · 1 hit paper
130 papers, 2.7k citations indexed

About

Shu Liao is a scholar working on Biomedical Engineering, Computer Vision and Pattern Recognition and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Shu Liao has authored 130 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Biomedical Engineering, 48 papers in Computer Vision and Pattern Recognition and 46 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Shu Liao's work include Medical Image Segmentation Techniques (41 papers), Atomic and Subatomic Physics Research (30 papers) and Characterization and Applications of Magnetic Nanoparticles (26 papers). Shu Liao is often cited by papers focused on Medical Image Segmentation Techniques (41 papers), Atomic and Subatomic Physics Research (30 papers) and Characterization and Applications of Magnetic Nanoparticles (26 papers). Shu Liao collaborates with scholars based in Taiwan, United States and China. Shu Liao's co-authors include Albert C. S. Chung, Meng Law, Dinggang Shen, Yaozong Gao, Aytekin Oto, H. E. Horng, Shieh‐Yueh Yang, Xiang Sean Zhou, Yiqiang Zhan and Qian Wang and has published in prestigious journals such as ACS Nano, Applied Physics Letters and PLoS ONE.

In The Last Decade

Shu Liao

126 papers receiving 2.6k citations

Hit Papers

Dominant Local Binary Patterns for Texture Classification 2009 2026 2014 2020 2009 200 400 600
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. Dominant Local Binary Patterns for Texture Classification
    2009 611 citations Shu Liao, Albert C. S. Chung et al. IEEE Transactions on Image Processing profile →

Peers

Shu Liao
Xiaodong Yang China
J.J. Vaquero Spain
Qiegen Liu China
Fei Shi China
Daniel S. Elson United Kingdom
Minhua Lu China
Liang Gao United States
Rongguang Liang United States
Norberto Malpica Spain
P.M. Shankar United States
Xiaodong Yang China
Shu Liao
Citations per year, relative to Shu Liao Shu Liao (= 1×) peers Xiaodong Yang

Countries citing papers authored by Shu Liao

Since Specialization
Citations

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

Fields of papers citing papers by Shu Liao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shu Liao

This figure shows the co-authorship network connecting the top 25 collaborators of Shu Liao. A scholar is included among the top collaborators of Shu Liao 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 Shu Liao. Shu Liao 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.
Xing, Yan, Wenli Qiao, Ying Wang, et al.. (2022). Deep learning-assisted PET imaging achieves fast scan/low-dose examination. EJNMMI Physics. 9(1). 7–7. 28 indexed citations
2.
Lee, Chiang‐Wen, Shu Liao, Yen‐Bin Liu, et al.. (2022). Highly efficient magnetic ablation and the contrast of various imaging using biocompatible liquid–metal gallium. BioMedical Engineering OnLine. 21(1). 38–38. 1 indexed citations
3.
Li, Danyan, Jie Gao, Qing Zhang, et al.. (2022). Deep Learning in Prostate Cancer Diagnosis Using Multiparametric Magnetic Resonance Imaging With Whole-Mount Histopathology Referenced Delineations. Frontiers in Medicine. 8. 810995–810995. 17 indexed citations
4.
Liao, Shu, et al.. (2022). Low cost multifunctional 3D printed image quality and dose verification phantom for an image-guided radiotherapy system. PLoS ONE. 17(4). e0266604–e0266604. 4 indexed citations
5.
Lin, Chin‐Wei, et al.. (2021). Improvement of multisource localization of magnetic particles in an animal. Scientific Reports. 11(1). 9628–9628. 1 indexed citations
6.
Wu, Jian-Kuen, et al.. (2021). Design of a motion simulation system to assist respiratory gating for radiation therapy. Medical dosimetry. 46(4). 360–363. 1 indexed citations
7.
Sheng, Ruofan, Liyun Zheng, Wei‐Zen Sun, et al.. (2021). Single-breath-hold T2WI liver MRI with deep learning-based reconstruction: A clinical feasibility study in comparison to conventional multi-breath-hold T2WI liver MRI. Magnetic Resonance Imaging. 81. 75–81. 40 indexed citations
8.
Wang, Shanshan, Guohua Cao, Shu Liao, et al.. (2021). Review and Prospect: Artificial Intelligence in Advanced Medical Imaging. PubMed. 1. 781868–781868. 55 indexed citations
9.
Liao, Shu, et al.. (2016). Automatic Lumbar SpondylolisthesisMeasurement in CT Images. IEEE Transactions on Medical Imaging. 35(7). 1658–1669. 15 indexed citations
10.
Yang, Shieh‐Yueh, Jen-Jie Chieh, C. C. Yang, et al.. (2013). HTS SQUID(高温超伝導-超伝導量子干渉素子)ベースAC磁気磁化率計を利用した超低濃度バイオマーカ検査における臨床利用. IEEE Transactions on Applied Superconductivity. 23. 1–4. 9 indexed citations
11.
Liao, Shu, Guorong Wu, & Dinggang Shen. (2012). A Statistical Framework for Inter-Group Image Registration. Neuroinformatics. 10(4). 367–378. 4 indexed citations
12.
Huang, Kai‐Wen, Hsin-Hsien Chen, H. E. Horng, et al.. (2012). Discriminating Hepatocellular Carcinoma in Rats Using a High-Tc SQUID Detected Nuclear Resonance Spectrometer in a Magnetic Shielding Box. PLoS ONE. 7(10). e47057–e47057. 4 indexed citations
13.
Liao, Shu, Yaozong Gao, Jun Lian, & Dinggang Shen. (2012). Sparse Patch-Based Label Propagation for Accurate Prostate Localization in CT Images. IEEE Transactions on Medical Imaging. 32(2). 419–434. 54 indexed citations
14.
Liao, Shu, et al.. (2011). A novel framework for longitudinal atlas construction with groupwise registration of subject image sequences. NeuroImage. 59(2). 1275–1289. 17 indexed citations
15.
Liao, Shu & Albert C. S. Chung. (2011). Nonrigid Brain MR Image Registration Using Uniform Spherical Region Descriptor. IEEE Transactions on Image Processing. 21(1). 157–169. 14 indexed citations
16.
Liao, Shu & Albert C. S. Chung. (2009). Non-rigid Image Registration with Uniform Gradient Spherical Patterns. Lecture notes in computer science. 12(Pt 1). 696–704. 1 indexed citations
17.
Chulu, Julius, Long H. Lee, Shu Liao, et al.. (2007). Apoptosis induction by avian reovirus through p53 and mitochondria-mediated pathway. Biochemical and Biophysical Research Communications. 356(3). 529–535. 46 indexed citations
18.
Liao, Shu, Max W. K. Law, & Albert C. S. Chung. (2006). Combining microscopic and macroscopic information for rotation and histogram equalization invariant texture classification. Lecture notes in computer science. 3851. 100–109. 5 indexed citations
19.
Yang, Hong-Chang, Chiu‐Hsien Wu, Jung-Chieh Chen, et al.. (2006). High-Tc superconducting quantum interference devices and biomagnetic applications. Journal of the Korean Physical Society. 48(95). 1084–1089. 2 indexed citations
20.
Yang, Shieh‐Yueh, et al.. (2006). Magnetic Nano-Particles and Their Applications in Immunoassays. Journal of the Korean Physical Society. 48(5). 999–1003. 5 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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