Jue Jiang

1.9k total citations
59 papers, 1.3k citations indexed

About

Jue Jiang is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Artificial Intelligence. According to data from OpenAlex, Jue Jiang has authored 59 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Radiology, Nuclear Medicine and Imaging, 16 papers in Pulmonary and Respiratory Medicine and 16 papers in Artificial Intelligence. Recurrent topics in Jue Jiang's work include Radiomics and Machine Learning in Medical Imaging (26 papers), Advanced Radiotherapy Techniques (13 papers) and Medical Imaging Techniques and Applications (9 papers). Jue Jiang is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (26 papers), Advanced Radiotherapy Techniques (13 papers) and Medical Imaging Techniques and Applications (9 papers). Jue Jiang collaborates with scholars based in United States, China and Japan. Jue Jiang's co-authors include Harini Veeraraghavan, Joseph O. Deasy, Yu‐Chi Hu, Neelam Tyagi, G Mageras, Andreas Rimner, Matthew D. Hellmann, Chia‐Ju Liu, Darragh Halpenny and Pengpeng Zhang and has published in prestigious journals such as Molecular Cell, International Journal of Molecular Sciences and IEEE Access.

In The Last Decade

Jue Jiang

53 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jue Jiang United States 18 658 349 343 275 174 59 1.3k
Romain Modzelewski France 24 1.1k 1.7× 123 0.4× 331 1.0× 506 1.8× 160 0.9× 70 1.8k
Alessandro Stefano Italy 25 880 1.3× 183 0.5× 207 0.6× 431 1.6× 81 0.5× 82 1.3k
Qi Song China 21 584 0.9× 298 0.9× 140 0.4× 207 0.8× 40 0.2× 61 1.4k
Lena Costaridou Greece 18 850 1.3× 506 1.4× 715 2.1× 380 1.4× 67 0.4× 86 1.5k
Jifke F. Veenland Netherlands 22 733 1.1× 136 0.4× 173 0.5× 418 1.5× 99 0.6× 51 1.4k
Marios A. Gavrielides United States 21 737 1.1× 187 0.5× 416 1.2× 400 1.5× 79 0.5× 68 1.2k
Jérôme Declerck United Kingdom 24 1.0k 1.6× 312 0.9× 127 0.4× 388 1.4× 58 0.3× 69 1.8k
Jonas Teuwen Netherlands 15 962 1.5× 120 0.3× 671 2.0× 268 1.0× 59 0.3× 62 1.4k
Avishek Chatterjee Netherlands 17 815 1.2× 77 0.2× 340 1.0× 283 1.0× 121 0.7× 38 1.2k
Junjie Hu China 17 678 1.0× 267 0.8× 224 0.7× 109 0.4× 148 0.9× 60 1.1k

Countries citing papers authored by Jue Jiang

Since Specialization
Citations

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

Fields of papers citing papers by Jue Jiang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jue Jiang

This figure shows the co-authorship network connecting the top 25 collaborators of Jue Jiang. A scholar is included among the top collaborators of Jue Jiang 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 Jue Jiang. Jue Jiang 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.
2.
Jiang, Jue, Justin Jee, Michele Waters, et al.. (2025). Large language model trained on clinical oncology data predicts cancer progression. npj Digital Medicine. 8(1). 397–397. 3 indexed citations
3.
Jiang, Jue, et al.. (2024). Artificial intelligence-based automated segmentation and radiotherapy dose mapping for thoracic normal tissues. Physics and Imaging in Radiation Oncology. 29. 100542–100542. 3 indexed citations
4.
Wang, Zenan, Ming Liu, Jue Jiang, & Xiaolei Qu. (2024). Colorectal polyp segmentation with denoising diffusion probabilistic models. Computers in Biology and Medicine. 180. 108981–108981. 1 indexed citations
5.
Paudyal, Ramesh, Jue Jiang, James E. Han, et al.. (2024). Auto-segmentation of neck nodal metastases using self-distilled masked image transformer on longitudinal MR images. PubMed. 1(1). ubae004–ubae004.
6.
Jiang, Jue, et al.. (2024). Self‐supervised learning improves robustness of deep learning lung tumor segmentation models to CT imaging differences. Medical Physics. 52(3). 1573–1588. 1 indexed citations
7.
Tian, Zhiqiang, et al.. (2024). HSC-T: B-Ultrasound-to-Elastography Translation via Hierarchical Structural Consistency Learning for Thyroid Cancer Diagnosis. IEEE Journal of Biomedical and Health Informatics. 29(2). 799–806.
8.
Luo, Shiwen, et al.. (2024). Breaking the barrier: Epigenetic strategies to combat platinum resistance in colorectal cancer. Drug Resistance Updates. 77. 101152–101152. 12 indexed citations
9.
Jiang, Jue, et al.. (2023). Deep learning‐based dominant index lesion segmentation for MR‐guided radiation therapy of prostate cancer. Medical Physics. 50(8). 4854–4870. 7 indexed citations
10.
Qu, Xiaolei, Zihao Wang, Dezhi Zheng, et al.. (2023). Complex Transformer Network for Single-Angle Plane-Wave Imaging. Ultrasound in Medicine & Biology. 49(10). 2234–2246. 2 indexed citations
11.
Jiang, Jue, et al.. (2022). Self-supervised 3D Anatomy Segmentation Using Self-distilled Masked Image Transformer (SMIT). Lecture notes in computer science. 13434. 556–566. 20 indexed citations
12.
Thor, Maria, Aditi Iyer, Jue Jiang, et al.. (2021). Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy. Physics and Imaging in Radiation Oncology. 19. 96–101. 13 indexed citations
13.
Qu, Xiaolei, et al.. (2020). An attention‐supervised full‐resolution residual network for the segmentation of breast ultrasound images. Medical Physics. 47(11). 5702–5714. 32 indexed citations
14.
Zhang, Pengpeng, Saad Nadeem, Jue Jiang, et al.. (2020). Predictive dose accumulation for HN adaptive radiotherapy. Physics in Medicine and Biology. 65(23). 235011–235011. 7 indexed citations
15.
Apte, Aditya, Aditi Iyer, Maria Thor, et al.. (2020). Library of deep-learning image segmentation and outcomes model-implementations. Physica Medica. 73. 190–196. 17 indexed citations
16.
Wang, Tianci, Jin Xu, Sanyun Wu, et al.. (2020). WEE1 inhibition induces glutamine addiction in T-cell acute lymphoblastic leukemia. Haematologica. 106(7). 1816–1827. 23 indexed citations
17.
Jiang, Jue, et al.. (2019). Adaptively Dense Feature Pyramid Network for Object Detection. IEEE Access. 7. 81132–81144. 13 indexed citations
18.
Elguindi, Sharif, Michael J. Zeléfsky, Jue Jiang, et al.. (2019). Deep learning-based auto-segmentation of targets and organs-at-risk for magnetic resonance imaging only planning of prostate radiotherapy. Physics and Imaging in Radiation Oncology. 12. 80–86. 75 indexed citations
19.
Jiang, Jue, Yu‐Chi Hu, Neelam Tyagi, et al.. (2018). Tumor-Aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation. Lecture notes in computer science. 11071. 777–785. 126 indexed citations
20.
Zhang, Hongli, Hua Wang, & Jue Jiang. (2013). Contrast-enhanced Ultrasound in Nodular Goiter. 29(6). 481–484. 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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