Antong Chen

635 total citations
28 papers, 219 citations indexed

About

Antong Chen is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Biomedical Engineering. According to data from OpenAlex, Antong Chen has authored 28 papers receiving a total of 219 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Vision and Pattern Recognition, 10 papers in Radiology, Nuclear Medicine and Imaging and 10 papers in Biomedical Engineering. Recurrent topics in Antong Chen's work include Medical Image Segmentation Techniques (10 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and AI in cancer detection (7 papers). Antong Chen is often cited by papers focused on Medical Image Segmentation Techniques (10 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and AI in cancer detection (7 papers). Antong Chen collaborates with scholars based in United States, Canada and China. Antong Chen's co-authors include Benoît M. Dawant, Matthew A. Deeley, Kenneth J. Niermann, Daniel Skomski, Xiangyu Ma, Mike Marsh, Hanmi Xi, Nicolas Piché, Luigi Moretti and Robert O. Williams and has published in prestigious journals such as IEEE Access, Industrial & Engineering Chemistry Research and Chemical Engineering Science.

In The Last Decade

Antong Chen

23 papers receiving 214 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Antong Chen United States 8 61 61 54 27 27 28 219
Georgi Gluhchev Bulgaria 10 165 2.7× 35 0.6× 41 0.8× 12 0.4× 42 1.6× 49 358
Rickard Sjögren Sweden 9 55 0.9× 69 1.1× 97 1.8× 7 0.3× 2 0.1× 23 390
Qiyuan Wang China 8 172 2.8× 59 1.0× 112 2.1× 11 0.4× 6 0.2× 22 373
Siddhartha Nath India 5 64 1.0× 26 0.4× 52 1.0× 7 0.3× 2 0.1× 12 264
Jie Tian China 9 104 1.7× 53 0.9× 145 2.7× 8 0.3× 3 0.1× 31 292
Zhiwei Qiao China 12 67 1.1× 92 1.5× 178 3.3× 29 1.1× 62 338
Florian Becker Germany 9 84 1.4× 25 0.4× 26 0.5× 47 1.7× 22 245
Artem Amirkhanov Austria 8 89 1.5× 78 1.3× 80 1.5× 10 0.4× 14 182
Shunyao Luan China 7 44 0.7× 63 1.0× 117 2.2× 23 0.9× 1 0.0× 13 230
Parisa Movahedi Finland 8 22 0.4× 38 0.6× 119 2.2× 15 0.6× 15 326

Countries citing papers authored by Antong Chen

Since Specialization
Citations

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

Fields of papers citing papers by Antong Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Antong Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Antong Chen. A scholar is included among the top collaborators of Antong Chen 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 Antong Chen. Antong Chen 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.
Liu, Yiqiao, Michal R. Tomaszewski, Shubing Wang, et al.. (2024). 3D Segmentation of Necrotic Lung Lesions in CT Images Using Self-Supervised Contrastive Learning. IEEE Access. 12. 32859–32869. 1 indexed citations
2.
3.
Liu, Yiqiao, Lin Li, Michal R. Tomaszewski, et al.. (2024). Universal 3D CT lesion segmentation using SAM with RECIST annotation. 4–4. 2 indexed citations
4.
Forest, Thomas, Kyathanahalli S. Janardhan, Michael A. Napolitano, et al.. (2024). Automated anomaly detection in histology images using deep learning. 21–21.
5.
Soukup, Jindřich, et al.. (2023). A deep learning solution for particle size analysis in low resolution inline microscopy images based on generative adversarial network. Powder Technology. 426. 118641–118641. 7 indexed citations
6.
Pan, Shaoyan, Yiqiao Liu, Michal R. Tomaszewski, et al.. (2023). Multi-dimension unified Swin Transformer for 3D Lesion Segmentation in Multiple Anatomical Locations. 1–5.
7.
Chen, Antong, et al.. (2021). Group Equivariant Generative Adversarial Networks. 1 indexed citations
8.
Ma, Xiangyu, Hanmi Xi, Antong Chen, et al.. (2020). Application of Deep Learning Convolutional Neural Networks for Internal Tablet Defect Detection: High Accuracy, Throughput, and Adaptability. Journal of Pharmaceutical Sciences. 109(4). 1547–1557. 68 indexed citations
9.
Chen, Antong, Bo Zhou, Jianda Yuan, et al.. (2020). A Deep Learning-Facilitated Radiomics Solution for the Prediction of Lung Lesion Shrinkage in Non-Small Cell Lung Cancer Trials. 678–682. 7 indexed citations
10.
11.
Zhang, Dongqing, Belma Dogdas, Smita Sampath, et al.. (2018). A multi-level convolutional LSTM model for the segmentation of left ventricle myocardium in infarcted porcine cine MR images. 470–473. 12 indexed citations
12.
Skomski, Daniel, Antong Chen, Ryan S. Teller, et al.. (2018). Theoretical Modeling and Mechanism of Drug Release from Long-Acting Parenteral Implants by Microstructural Image Characterization. Industrial & Engineering Chemistry Research. 6 indexed citations
13.
Dogdas, Belma, et al.. (2017). Automatic segmentation of left ventricle in cardiac cine MRI images based on deep learning. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10133. 101331W–101331W. 7 indexed citations
14.
Chen, Antong, et al.. (2017). Data-driven model and model paradigm to predict 1D and 2D particle size distribution from measured chord-length distribution. Chemical Engineering Science. 164. 202–218. 22 indexed citations
15.
16.
Chen, Antong, Catherine D. G. Hines, Belma Dogdas, et al.. (2015). Targeting of deep-brain structures in nonhuman primates using MR and CT Images. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9415. 94152K–94152K. 3 indexed citations
17.
Chen, Antong, Jack H. Noble, Kenneth J. Niermann, Matthew A. Deeley, & Benoît M. Dawant. (2012). Segmentation of parotid glands in head and neck CT images using a constrained active shape model with landmark uncertainty. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8314. 83140P–83140P. 5 indexed citations
18.
Chen, Antong, Kenneth J. Niermann, Matthew A. Deeley, & Benoît M. Dawant. (2011). Evaluation of multi atlas-based approaches for the segmentation of the thyroid gland in IMRT head-and-neck CT images. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7962. 796224–796224. 7 indexed citations
19.
Chen, Antong, Matthew A. Deeley, Kenneth J. Niermann, Luigi Moretti, & Benoît M. Dawant. (2010). Combining registration and active shape models for the automatic segmentation of the lymph node regions in head and neck CT images. Medical Physics. 37(12). 6338–6346. 36 indexed citations
20.
Chen, Antong, Matthew A. Deeley, Kenneth J. Niermann, Luigi Moretti, & Benoît M. Dawant. (2010). Segmentation of lymph node regions in head-and-neck CT images using a combination of registration and active shape model. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7623. 76231Q–76231Q. 1 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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