Shih-Ying Huang

1.1k total citations · 1 hit paper
15 papers, 776 citations indexed

About

Shih-Ying Huang is a scholar working on Radiology, Nuclear Medicine and Imaging, Neurology and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Shih-Ying Huang has authored 15 papers receiving a total of 776 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Radiology, Nuclear Medicine and Imaging, 3 papers in Neurology and 3 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Shih-Ying Huang's work include Medical Imaging Techniques and Applications (5 papers), Radiopharmaceutical Chemistry and Applications (3 papers) and Plant and animal studies (3 papers). Shih-Ying Huang is often cited by papers focused on Medical Imaging Techniques and Applications (5 papers), Radiopharmaceutical Chemistry and Applications (3 papers) and Plant and animal studies (3 papers). Shih-Ying Huang collaborates with scholars based in United States, Taiwan and India. Shih-Ying Huang's co-authors include Youngho Seo, Randall A. Hawkins, John M. Boone, Karen K. Lindfors, Roy Harnish, Benjamin L. Franc, Spencer C. Behr, Timothy P. Copeland, Miguel Hernandez Pampaloni and Wayne L. Monsky and has published in prestigious journals such as Radiology, Analytica Chimica Acta and Medical Physics.

In The Last Decade

Shih-Ying Huang

14 papers receiving 759 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.

A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using ¹⁸F-FDG PET of the Brain

2018 379 citations Yiming Ding, Jae Ho Sohn et al. Radiology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shih-Ying Huang United States	9	439	185	172	142	110	15	776
Virginia Hill United States	15	486 1.1×	88 0.5×	112 0.7×	96 0.7×	99 0.9×	27	674
Meihao Wang China	15	570 1.3×	296 1.6×	162 0.9×	73 0.5×	48 0.4×	51	868
Luke Macyszyn United States	16	410 0.9×	94 0.5×	83 0.5×	193 1.4×	85 0.8×	44	975
Roy Harnish United States	11	296 0.7×	162 0.9×	43 0.3×	117 0.8×	111 1.0×	14	721
Gianluca Brugnara Germany	11	424 1.0×	74 0.4×	134 0.8×	68 0.5×	129 1.2×	32	778
Jiahong Ouyang United States	14	309 0.7×	92 0.5×	104 0.6×	136 1.0×	59 0.5×	34	626
Jiechao Ma China	15	374 0.9×	142 0.8×	222 1.3×	60 0.4×	37 0.3×	30	734
Michael Tran Duong United States	12	201 0.5×	67 0.4×	43 0.3×	88 0.6×	97 0.9×	25	769
Emily B. Ambinder United States	12	371 0.8×	254 1.4×	127 0.7×	53 0.4×	88 0.8×	67	825
Shahriar Faghani United States	16	297 0.7×	141 0.8×	57 0.3×	130 0.9×	57 0.5×	57	680

Countries citing papers authored by Shih-Ying Huang

Since Specialization

Citations

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

Fields of papers citing papers by Shih-Ying Huang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shih-Ying Huang

This figure shows the co-authorship network connecting the top 25 collaborators of Shih-Ying Huang. A scholar is included among the top collaborators of Shih-Ying 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 Shih-Ying Huang. Shih-Ying Huang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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15 of 15 papers shown

Huang, Shih-Ying, et al.. (2024). Hybrids of two destructive subterranean termites established in the field, revealing a potential for gene flow between species. Heredity. 132(5). 257–266. 2 indexed citations

Huang, Shih-Ying, et al.. (2022). Nationwide Termite Pest Survey Conducted in Taiwan as a Citizen Science Project. Journal of Economic Entomology. 115(5). 1650–1658. 7 indexed citations

Huang, Jia‐Hsin, et al.. (2021). Termite Pest Identification Method Based on Deep Convolution Neural Networks. Journal of Economic Entomology. 114(6). 2452–2459. 5 indexed citations

Aboian, Mariam, Shih-Ying Huang, Miguel Hernandez‐Pampaloni, et al.. (2020). ¹²⁴I-MIBG PET/CT to Monitor Metastatic Disease in Children with Relapsed Neuroblastoma. Journal of Nuclear Medicine. 62(1). 43–47. 30 indexed citations

Seo, Youngho, Shih-Ying Huang, Randall A. Hawkins, et al.. (2019). Technical Note: Simplified and practical pretherapy tumor dosimetry — A feasibility study for ¹³¹I‐MIBG therapy of neuroblastoma using ¹²⁴I‐MIBG PET/CT. Medical Physics. 46(5). 2477–2486. 11 indexed citations

Ding, Yiming, Jae Ho Sohn, Michael Kawczynski, et al.. (2018). A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using ¹⁸F-FDG PET of the Brain. Radiology. 290(2). 456–464. 379 indexed citations breakdown →

Huang, Shih-Ying, Benjamin L. Franc, Roy Harnish, et al.. (2018). Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis. npj Breast Cancer. 4(1). 24–24. 77 indexed citations

Huang, Shih-Ying, et al.. (2017). Simultaneous detections of genetic fragment and single nucleotide mutation with a three-tiered output for tuberculosis diagnosis. Analytica Chimica Acta. 1007. 1–9. 3 indexed citations

Woo, Sang‐Keun, Kwang Il Kim, Youngho Seo, et al.. (2016). Internal dosimetry calculation of Cu-64 NOTA trastuzumab in human breast cancer xenograft mouse using Monte Carlo simulation.. 57. 1401–1401. 1 indexed citations

10.

Huang, Shih-Ying, et al.. (2015). Facile Fabrication of a Sensor with a Bifunctional Interface for Logic Analysis of the New Delhi Metallo-β-Lactamase (NDM)-Coding Gene. ACS Sensors. 1(2). 124–130. 18 indexed citations

11.

Huang, Shih-Ying, et al.. (2014). The effect of magnetic field on positron range and spatial resolution in an integrated whole-body time-of-flight PET/MRI system. PubMed. 2014. 1–4. 16 indexed citations

12.

Huang, Shih-Ying, Wesley E. Bolch, Choonsik Lee, et al.. (2014). Patient-Specific Dosimetry Using Pretherapy [124I]m-iodobenzylguanidine ([124I]mIBG) Dynamic PET/CT Imaging Before [131I]mIBG Targeted Radionuclide Therapy for Neuroblastoma. Molecular Imaging and Biology. 17(2). 284–294. 50 indexed citations

13.

Boone, John M., Kai Yang, George W. Burkett, et al.. (2010). An X-ray Computed Tomography/Positron Emission Tomography System Designed Specifically for Breast Imaging. Technology in Cancer Research & Treatment. 9(1). 29–43. 15 indexed citations

14.

Prionas, Nicolas D., Karen K. Lindfors, Shonket Ray, et al.. (2010). Contrast-enhanced Dedicated Breast CT: Initial Clinical Experience. Radiology. 256(3). 714–723. 162 indexed citations

15.

Huang, Shih-Ying. (1985). Observation on the structure of dentinal tubules in the superficial layer of human dentin.. THE JOURNAL OF THE STOMATOLOGICAL SOCIETY JAPAN. 52(2). 438–468.

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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