Lie Ju

814 total citations
23 papers, 329 citations indexed

About

Lie Ju is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Ophthalmology. According to data from OpenAlex, Lie Ju has authored 23 papers receiving a total of 329 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Computer Vision and Pattern Recognition and 7 papers in Ophthalmology. Recurrent topics in Lie Ju's work include Retinal Imaging and Analysis (9 papers), Retinal and Optic Conditions (5 papers) and Digital Imaging for Blood Diseases (4 papers). Lie Ju is often cited by papers focused on Retinal Imaging and Analysis (9 papers), Retinal and Optic Conditions (5 papers) and Digital Imaging for Blood Diseases (4 papers). Lie Ju collaborates with scholars based in Australia, China and United Arab Emirates. Lie Ju's co-authors include Zongyuan Ge, Xin Wang, Lin Wang, Xin Zhao, Dwarikanath Mahapatra, Jinrong He, Cheng Cai, Quan Zhou, Tongliang Liu and Huimin Lu and has published in prestigious journals such as IEEE Transactions on Medical Imaging, British Journal of Ophthalmology and Computers and Electronics in Agriculture.

In The Last Decade

Lie Ju

21 papers receiving 323 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Lie Ju Australia	10	164	101	90	90	32	23	329
Rajeev Kumar Singh India	9	138 0.8×	41 0.4×	60 0.7×	132 1.5×	30 0.9×	25	380
Tianxia Gong Singapore	8	169 1.0×	177 1.8×	39 0.4×	70 0.8×	29 0.9×	17	296
Along He China	6	289 1.8×	140 1.4×	248 2.8×	128 1.4×	14 0.4×	14	518
Jahanzaib Latif China	8	185 1.1×	79 0.8×	93 1.0×	106 1.2×	8 0.3×	9	338
Rahim Khan China	9	112 0.7×	5 0.0×	66 0.7×	124 1.4×	7 0.2×	19	365
Tati Rajab Mengko Indonesia	7	133 0.8×	64 0.6×	130 1.4×	41 0.5×	6 0.2×	36	289
Adeel M. Syed Pakistan	9	185 1.1×	145 1.4×	85 0.9×	44 0.5×	5 0.2×	18	286
Lingjiao Pan China	10	149 0.9×	56 0.6×	172 1.9×	51 0.6×	7 0.2×	27	313
Sundaresan Raman India	10	81 0.5×	49 0.5×	77 0.9×	38 0.4×	7 0.2×	28	282
Sunil Kumar Yadav Germany	10	63 0.4×	50 0.5×	56 0.6×	19 0.2×	9 0.3×	28	256

Countries citing papers authored by Lie Ju

Since Specialization

Citations

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

Fields of papers citing papers by Lie Ju

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lie Ju

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Yu, Zhen, Lie Ju, Yaniv Gal, et al.. (2025). Hierarchical skin lesion image classification with prototypical decision tree. npj Digital Medicine. 8(1). 26–26.

Tang, Feilong, Huanfen Zhou, Yun Cai, et al.. (2025). Forecasting the diabetic retinopathy progression using generative adversarial networks. Communications Medicine. 5(1). 368–368. 1 indexed citations

Ju, Lie, et al.. (2025). Towards Realistic Semi-supervised Medical Image Classification. Proceedings of the AAAI Conference on Artificial Intelligence. 39(5). 4968–4976.

Yu, Zhen, Chi Liu, Lie Ju, et al.. (2024). Prompt-Driven Latent Domain Generalization for Medical Image Classification. IEEE Transactions on Medical Imaging. 44(1). 348–360. 2 indexed citations

Wu, Yicheng, Xiangde Luo, Zhe Xu, et al.. (2024). Diversified and Personalized Multi-Rater Medical Image Segmentation. 11470–11479. 9 indexed citations

Shi, Xu, Lie Ju, Li Dong, et al.. (2024). Deep Learning Models for the Screening of Cognitive Impairment Using Multimodal Fundus Images. Ophthalmology Retina. 8(7). 666–677. 6 indexed citations

Ju, Lie, et al.. (2024). Deep Learning Detection of Early Retinal Peripheral Degeneration From Ultra-Widefield Fundus Photographs of Asymptomatic Young Adult (17–19 Years) Candidates to Airforce Cadets. Translational Vision Science & Technology. 13(2). 1–1. 3 indexed citations

Dong, Li, Lie Ju, Xue Jiang, et al.. (2023). Retinal photograph-based deep learning system for detection of hyperthyroidism: a multicenter, diagnostic study. Journal Of Big Data. 10(1). 2 indexed citations

Shen, Yang, Lin Wang, Xun Chen, et al.. (2023). Effect of vault on predicting postoperative refractive error for posterior chamber phakic intraocular lens based on a machine learning model. Journal of Cataract & Refractive Surgery. 50(4). 319–327. 2 indexed citations

10.

Huang, Qiujing, et al.. (2023). Development and validation of a semi-supervised deep learning model for automatic retinopathy of prematurity staging. iScience. 27(1). 108516–108516. 5 indexed citations

11.

Feng, Wei, et al.. (2023). Unsupervised Domain Adaptation for Medical Image Segmentation by Selective Entropy Constraints and Adaptive Semantic Alignment. Proceedings of the AAAI Conference on Artificial Intelligence. 37(1). 623–631. 10 indexed citations

12.

Wang, Lin, Xiufen Ye, Lie Ju, et al.. (2023). Medical matting: Medical image segmentation with uncertainty from the matting perspective. Computers in Biology and Medicine. 158. 106714–106714. 8 indexed citations

13.

Ren, Xiang, Wei Feng, Yu Lin, et al.. (2022). Artificial intelligence to distinguish retinal vein occlusion patients using color fundus photographs. Eye. 37(10). 2026–2032. 15 indexed citations

14.

Huang, Xiao, Lie Ju, Jian Li, et al.. (2022). An Intelligent Diagnostic System for Thyroid-Associated Ophthalmopathy Based on Facial Images. Frontiers in Medicine. 9. 920716–920716. 27 indexed citations

15.

Wang, Lin, Xiufen Ye, Donghao Zhang, et al.. (2022). 3D matting: A benchmark study on soft segmentation method for pulmonary nodules applied in computed tomography. Computers in Biology and Medicine. 150. 106153–106153. 1 indexed citations

16.

Wang, Xin, Lin Wang, Lie Ju, et al.. (2021). Incremental learning for exudate and hemorrhage segmentation on fundus images. Information Fusion. 73. 157–164. 26 indexed citations

17.

Shen, Yang, Lin Wang, Jianmin Shang, et al.. (2021). Big-data and artificial-intelligence-assisted vault prediction and EVO-ICL size selection for myopia correction. British Journal of Ophthalmology. 107(2). 201–206. 48 indexed citations

18.

Ju, Lie, Xin Wang, Xin Zhao, et al.. (2021). Synergic Adversarial Label Learning for Grading Retinal Diseases via Knowledge Distillation and Multi-Task Learning. IEEE Journal of Biomedical and Health Informatics. 25(10). 3709–3720. 29 indexed citations

19.

Zhang, Ting, Lie Ju, Jingjing Zhai, et al.. (2019). miRLocator: A Python Implementation and Web Server for Predicting miRNAs from Pre-miRNA Sequences. Methods in molecular biology. 1932. 89–97. 4 indexed citations

20.

Cai, Cheng, et al.. (2019). Deep cascaded convolutional models for cattle pose estimation. Computers and Electronics in Agriculture. 164. 104885–104885. 50 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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