Linmin Pei

2.0k total citations
11 papers, 304 citations indexed

About

Linmin Pei is a scholar working on Neurology, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Linmin Pei has authored 11 papers receiving a total of 304 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Neurology, 6 papers in Computer Vision and Pattern Recognition and 6 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Linmin Pei's work include Brain Tumor Detection and Classification (9 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Medical Image Segmentation Techniques (5 papers). Linmin Pei is often cited by papers focused on Brain Tumor Detection and Classification (9 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Medical Image Segmentation Techniques (5 papers). Linmin Pei collaborates with scholars based in United States, China and Taiwan. Linmin Pei's co-authors include Khan M. Iftekharuddin, Lasitha Vidyaratne, Syed M. S. Reza, Christos Davatzikos, Arastoo Vossough, James Y. Chen, Karra A. Jones, Spyridon Bakas, Mahbubul Alam and Rivka R. Colen and has published in prestigious journals such as Scientific Reports, Frontiers in Neuroscience and Frontiers in Oncology.

In The Last Decade

Linmin Pei

10 papers receiving 295 citations

Peers

Linmin Pei
B. Kiran Madhusudhan India
Syed M. S. Reza United States
Nelly Gordillo Mexico
Chandan Ganesh Bangalore Yogananda United States
Rachida Saouli Algeria
Gopal S. Tandel India
Jason Cheng United States
Mostefa Ben Naceur Algeria
Changhong Liang China
Esther Alberts Germany
B. Kiran Madhusudhan India
Linmin Pei
Citations per year, relative to Linmin Pei Linmin Pei (= 1×) peers B. Kiran Madhusudhan

Countries citing papers authored by Linmin Pei

Since Specialization
Citations

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

Fields of papers citing papers by Linmin Pei

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Linmin Pei

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

All Works

11 of 11 papers shown
1.
2.
Murugesan, Gowtham Krishnan, Mariam Aboian, Keyvan Farahani, et al.. (2024). AI-Generated Annotations Dataset for Diverse Cancer Radiology Collections in NCI Image Data Commons. Scientific Data. 11(1). 1165–1165. 2 indexed citations
3.
Pei, Linmin, et al.. (2022). A general skull stripping of multiparametric brain MRIs using 3D convolutional neural network. Scientific Reports. 12(1). 10826–10826. 16 indexed citations
4.
Guo, Jing-Ming, et al.. (2022). A weakly supervised deep learning-based method for glioma subtype classification using WSI and mpMRIs. Scientific Reports. 12(1). 6111–6111. 30 indexed citations
5.
Pei, Linmin, et al.. (2021). Deep Neural Network Analysis of Pathology Images With Integrated Molecular Data for Enhanced Glioma Classification and Grading. Frontiers in Oncology. 11. 668694–668694. 42 indexed citations
6.
Pei, Linmin, et al.. (2020). Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images. Scientific Reports. 10(1). 19726–19726. 99 indexed citations
7.
Pei, Linmin, et al.. (2020). Deep learning with context encoding for semantic brain tumor segmentation and patient survival prediction. ODU Digital Commons (Old Dominion University). 16–16. 3 indexed citations
8.
Pei, Linmin, Spyridon Bakas, Arastoo Vossough, et al.. (2019). Longitudinal brain tumor segmentation prediction in MRI using feature and label fusion. Biomedical Signal Processing and Control. 55. 101648–101648. 47 indexed citations
9.
Alam, Mahbubul, et al.. (2019). Feature-Guided Deep Radiomics for Glioblastoma Patient Survival Prediction. Frontiers in Neuroscience. 13. 966–966. 42 indexed citations
10.
Pei, Linmin, Syed M. S. Reza, Wei Li, Christos Davatzikos, & Khan M. Iftekharuddin. (2017). Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10134. 101342L–101342L. 17 indexed citations
11.
Pei, Linmin, Syed M. S. Reza, & Khan M. Iftekharuddin. (2015). Improved brain tumor growth prediction and segmentation in longitudinal brain MRI. 421–424. 6 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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