Chung‐Ming Lo

1.6k total citations
62 papers, 1.2k citations indexed

About

Chung‐Ming Lo is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Chung‐Ming Lo has authored 62 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Radiology, Nuclear Medicine and Imaging, 24 papers in Artificial Intelligence and 14 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Chung‐Ming Lo's work include AI in cancer detection (22 papers), Radiomics and Machine Learning in Medical Imaging (19 papers) and Ultrasound Imaging and Elastography (8 papers). Chung‐Ming Lo is often cited by papers focused on AI in cancer detection (22 papers), Radiomics and Machine Learning in Medical Imaging (19 papers) and Ultrasound Imaging and Elastography (8 papers). Chung‐Ming Lo collaborates with scholars based in Taiwan, South Korea and United States. Chung‐Ming Lo's co-authors include Ruey‐Feng Chang, Kevin Li‐Chun Hsieh, Chiun‐Sheng Huang, Jeon‐Hor Chen, Woo Kyung Moon, Yeun‐Chung Chang, Jung Min Chang, Cheng‐Yu Chen, Honghao Chen and Daw-Tung Lin and has published in prestigious journals such as PLoS ONE, IEEE Access and IEEE Journal on Selected Areas in Communications.

In The Last Decade

Chung‐Ming Lo

60 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chung‐Ming Lo Taiwan 21 746 564 284 225 197 62 1.2k
Qi Song China 21 584 0.8× 140 0.2× 298 1.0× 120 0.5× 207 1.1× 61 1.4k
Jyoti Kini India 16 326 0.4× 495 0.9× 279 1.0× 68 0.3× 65 0.3× 69 828
Gabriel Chartrand Canada 14 823 1.1× 379 0.7× 268 0.9× 96 0.4× 193 1.0× 19 1.6k
Georgios Z. Papadakis United States 20 582 0.8× 252 0.4× 126 0.4× 74 0.3× 347 1.8× 83 1.6k
Yali Zang China 20 1.6k 2.1× 411 0.7× 244 0.9× 76 0.3× 926 4.7× 42 2.1k
Hidetaka Arimura Japan 19 819 1.1× 245 0.4× 185 0.7× 136 0.6× 547 2.8× 123 1.3k
Xuxin Chen United States 11 400 0.5× 307 0.5× 157 0.6× 81 0.4× 129 0.7× 40 858
Longzhong Liu China 20 506 0.7× 414 0.7× 217 0.8× 64 0.3× 145 0.7× 46 1.3k
Raphael Meier Switzerland 16 455 0.6× 254 0.5× 219 0.8× 241 1.1× 114 0.6× 36 1.0k
Jie Tian China 16 765 1.0× 324 0.6× 181 0.6× 49 0.2× 371 1.9× 39 1.2k

Countries citing papers authored by Chung‐Ming Lo

Since Specialization
Citations

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

Fields of papers citing papers by Chung‐Ming Lo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chung‐Ming Lo. 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 Chung‐Ming Lo. The network helps show where Chung‐Ming Lo may publish in the future.

Co-authorship network of co-authors of Chung‐Ming Lo

This figure shows the co-authorship network connecting the top 25 collaborators of Chung‐Ming Lo. A scholar is included among the top collaborators of Chung‐Ming Lo 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 Chung‐Ming Lo. Chung‐Ming Lo 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.
Lo, Chung‐Ming, et al.. (2024). Automated optical inspection based on synthetic mechanisms combining deep learning and machine learning. Journal of Intelligent Manufacturing. 36(7). 4769–4783. 5 indexed citations
2.
Lo, Chung‐Ming, et al.. (2024). Interactive content-based image retrieval with deep learning for CT abdominal organ recognition. Physics in Medicine and Biology. 69(4). 45004–45004. 8 indexed citations
3.
Lo, Chung‐Ming & Kuo‐Lung Lai. (2024). Deep Image Guiding: Guide Knee Ultrasound Scanning Using Hierarchical Classification and Retrieval. IEEE Transactions on Instrumentation and Measurement. 73. 1–9. 3 indexed citations
4.
Lo, Chung‐Ming & Kuo‐Lung Lai. (2024). Septic Arthritis Modeling Using Sonographic Fusion with Attention and Selective Transformation: a Preliminary Study. Journal of Imaging Informatics in Medicine. 38(2). 1028–1039. 1 indexed citations
5.
Lo, Chung‐Ming, Jeng‐Kai Jiang, & Chun‐Chi Lin. (2024). Detecting microsatellite instability in colorectal cancer using Transformer-based colonoscopy image classification and retrieval. PLoS ONE. 19(1). e0292277–e0292277. 6 indexed citations
6.
Lo, Chung‐Ming & Kuo‐Lung Lai. (2023). Deep learning-based assessment of knee septic arthritis using transformer features in sonographic modalities. Computer Methods and Programs in Biomedicine. 237. 107575–107575. 20 indexed citations
7.
8.
Lo, Chung‐Ming & Kuo‐Lung Lai. (2023). Deep Learning-Based Assessment of Knee Septic Arthritis Using Transformer Features in Sonographic Modalities. SSRN Electronic Journal. 2 indexed citations
9.
Lo, Chung‐Ming, Jen‐Kou Lin, Tzu‐Chen Lin, et al.. (2023). Modeling the survival of colorectal cancer patients based on colonoscopic features in a feature ensemble vision transformer. Computerized Medical Imaging and Graphics. 107. 102242–102242. 22 indexed citations
10.
Lo, Chung‐Ming, et al.. (2023). Predictive stroke risk model with vision transformer‐based Doppler features. Medical Physics. 51(1). 126–138. 9 indexed citations
11.
Lo, Chung‐Ming, et al.. (2022). Computer-aided diagnosis of ischemic stroke using multi-dimensional image features in carotid color Doppler. Computers in Biology and Medicine. 147. 105779–105779. 19 indexed citations
12.
Lo, Chung‐Ming, et al.. (2022). Rapid Polyp Classification in Colonoscopy Using Textural and Convolutional Features. Healthcare. 10(8). 1494–1494. 16 indexed citations
13.
Lo, Chung‐Ming, et al.. (2021). Rapid Assessment of Acute Ischemic Stroke by Computed Tomography Using Deep Convolutional Neural Networks. Journal of Digital Imaging. 34(3). 637–646. 44 indexed citations
14.
Lo, Chung‐Ming, et al.. (2020). Quantitative Analysis of Melanosis Coli Colonic Mucosa Using Textural Patterns. Applied Sciences. 10(1). 404–404. 3 indexed citations
15.
Lo, Chung‐Ming, et al.. (2019). Intelligent Glioma Grading Based on Deep Transfer Learning of MRI Radiomic Features. Applied Sciences. 9(22). 4926–4926. 16 indexed citations
16.
Lo, Chung‐Ming, et al.. (2018). A machine learning texture model for classifying lung cancer subtypes using preliminary bronchoscopic findings. Medical Physics. 45(12). 5509–5514. 17 indexed citations
17.
Huang, Chiun‐Sheng, et al.. (2017). Whole-Breast Ultrasound for Breast Screening and Archiving. Ultrasound in Medicine & Biology. 43(5). 926–933. 8 indexed citations
18.
Chang, Ruey‐Feng, Honghao Chen, Yeun‐Chung Chang, et al.. (2016). Quantification of breast tumor heterogeneity for ER status, HER2 status, and TN molecular subtype evaluation on DCE-MRI. Magnetic Resonance Imaging. 34(6). 809–819. 74 indexed citations
19.
Hsieh, Kevin Li‐Chun, et al.. (2016). Computer-aided grading of gliomas based on local and global MRI features. Computer Methods and Programs in Biomedicine. 139. 31–38. 79 indexed citations
20.
Moon, Woo Kyung, Chung‐Ming Lo, Rong‐Tai Chen, et al.. (2014). Tumor detection in automated breast ultrasound images using quantitative tissue clustering. Medical Physics. 41(4). 42901–42901. 51 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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