Junlin Yang

816 total citations
13 papers, 262 citations indexed

About

Junlin Yang is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Junlin Yang has authored 13 papers receiving a total of 262 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 7 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Junlin Yang's work include Radiomics and Machine Learning in Medical Imaging (5 papers), COVID-19 diagnosis using AI (4 papers) and Domain Adaptation and Few-Shot Learning (4 papers). Junlin Yang is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (5 papers), COVID-19 diagnosis using AI (4 papers) and Domain Adaptation and Few-Shot Learning (4 papers). Junlin Yang collaborates with scholars based in United States, China and Switzerland. Junlin Yang's co-authors include Antonio Torralba, Daiqing Li, Sanja Fidler, Karsten Kreis, Julius Chapiro, MingDe Lin, James S. Duncan, Nicha C. Dvornek, Fan Zhang and Fan Zhang and has published in prestigious journals such as IEEE Transactions on Medical Imaging, Applied Sciences and Journal of Vascular and Interventional Radiology.

In The Last Decade

Junlin Yang

12 papers receiving 256 citations

Peers

Junlin Yang
Seung Yeon Shin United States
Gabriel Efrain Humpire Mamani Netherlands
Sonit Singh Australia
Shilei Cao China
Lisa Di Jorio Canada
Ja-Yeon Jeong South Korea
Tianyu Shi China
Grzegorz Chlebus Germany
Seung Yeon Shin United States
Junlin Yang
Citations per year, relative to Junlin Yang Junlin Yang (= 1×) peers Seung Yeon Shin

Countries citing papers authored by Junlin Yang

Since Specialization
Citations

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

Fields of papers citing papers by Junlin Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Junlin Yang

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

All Works

13 of 13 papers shown
1.
Yang, Junlin, Nicha C. Dvornek, Aurélie Pahud de Mortanges, et al.. (2024). Prior knowledge-guided vision-transformer-based unsupervised domain adaptation for intubation prediction in lung disease at one week. Computerized Medical Imaging and Graphics. 118. 102442–102442.
2.
Yang, Junlin, et al.. (2023). Research on Lightweight Model for Rapid Identification of Chunky Food Based on Machine Vision. Applied Sciences. 13(15). 8781–8781. 4 indexed citations
3.
Zeevi, Tal, Junlin Yang, Yanhong Deng, et al.. (2022). MR Imaging Biomarkers for the Prediction of Outcome after Radiofrequency Ablation of Hepatocellular Carcinoma: Qualitative and Quantitative Assessments of the Liver Imaging Reporting and Data System and Radiomic Features. Journal of Vascular and Interventional Radiology. 33(7). 814–824.e3. 12 indexed citations
4.
Malpani, Rohil, Junlin Yang, Tal Zeevi, et al.. (2021). Quantitative Automated Segmentation of Lipiodol Deposits on Cone-Beam CT Imaging Acquired during Transarterial Chemoembolization for Liver Tumors: A Deep Learning Approach. Journal of Vascular and Interventional Radiology. 33(3). 324–332.e2. 7 indexed citations
5.
Li, Daiqing, Junlin Yang, Karsten Kreis, Antonio Torralba, & Sanja Fidler. (2021). Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization. 8296–8307. 115 indexed citations
6.
Liu, Zhicheng, Xiaodong Yi, Feixue Gao, et al.. (2021). Green Carbon Science: A Scientific Basis for Achieving ‘Dual Carbon’ Goal--Academic Summary of the 292nd “Shuang-Qing Forum”. Acta Physico-Chimica Sinica. 0(0). 2112029–0. 4 indexed citations
7.
Zhang, Fan, Nicha C. Dvornek, Junlin Yang, Julius Chapiro, & James S. Duncan. (2020). Layer Embedding Analysis in Convolutional Neural Networks for Improved Probability Calibration and Classification. IEEE Transactions on Medical Imaging. 39(11). 3331–3342. 7 indexed citations
8.
Yang, Junlin, Nicha C. Dvornek, Fan Zhang, et al.. (2019). Domain-Agnostic Learning With Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation. PubMed. 2019. 323–331. 8 indexed citations
9.
Zhuang, Juntang, Nicha C. Dvornek, Xiaoxiao Li, Junlin Yang, & James S. Duncan. (2019). Decision explanation and feature importance for invertible networks. PubMed. 521. 4235–4239. 1 indexed citations
10.
Yang, Junlin, Nicha C. Dvornek, Fan Zhang, et al.. (2019). Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation. Lecture notes in computer science. 11765. 255–263. 67 indexed citations
11.
12.
Zhuang, Juntang & Junlin Yang. (2018). ShelfNet for Real-time Semantic Segmentation. arXiv (Cornell University). 5 indexed citations
13.
Zhang, Fan, Junlin Yang, Nariman Nezami, et al.. (2018). Liver Tissue Classification Using an Auto-context-based Deep Neural Network with a Multi-phase Training Framework. Lecture notes in computer science. 11075. 59–66. 20 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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2026