Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
20164.0k citationsHoo-Chang Shin, Le Lü et al.IEEE Transactions on Medical Imagingprofile →
Improving Computer-Aided Detection UsingConvolutional Neural Networks and Random View Aggregation
2015417 citationsLe Lü, Jianhua Yao et al.IEEE Transactions on Medical Imagingprofile →
DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning
This map shows the geographic impact of Le Lü'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 Le Lü with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Le Lü more than expected).
This network shows the impact of papers produced by Le Lü. 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 Le Lü. The network helps show where Le Lü may publish in the future.
Co-authorship network of co-authors of Le Lü
This figure shows the co-authorship network connecting the top 25 collaborators of Le Lü.
A scholar is included among the top collaborators of Le Lü 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 Le Lü. Le Lü is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yan, Ke, Le Lü, & Ronald M. Summers. (2017). Unsupervised body part regression using convolutional neural network with self-organization.. arXiv (Cornell University).3 indexed citations
Lü, Le. (2014). Combinational Stock Price Forecasting Based on Multiple Regression and Technical Analysis.
12.
Lü, Le. (2013). Effect and Mechanism of Human Hepatocarcinoma Cell Bel-7402 Apoptosis Induced by Cinnamic Acid. Journal of Sun Yat-sen University.2 indexed citations
13.
Lü, Le, Yong Li, Yichun Qiao, et al.. (2013). [Association between polymorphism of FOXA1 gene and type 2 diabetes].. PubMed. 42(5). 736–40.1 indexed citations
Lü, Le. (2009). Enteral nutrition support at early period used in patients with liver cancer after liver transplantation. Chinese Clinical Oncology.1 indexed citations
16.
Lü, Le. (2008). Identification of hydrogeological parameters based on the Bayesian method. Shuiwen dizhi gongcheng dizhi.2 indexed citations
17.
Lü, Le. (2008). The Development and Change of The Book Binding Design during the Period of Republic of China.
18.
Lü, Le & Ilan Alon. (2004). Analysis of the Changing Trends in Attitudes and Values of the Chinese - The Case of Shanghai"s Young & Educated. The Journal of International and Area Studies. 11. 67–88.10 indexed citations
19.
Lü, Le, Gregory D. Hager, & Laurent Younès. (2004). A Three Tiered Approach for Articulated Object Action Modeling and Recognition. Neural Information Processing Systems. 17. 841–848.2 indexed citations
20.
Lü, Le, et al.. (2001). Model- and Exemplar-based Robust Head Pose Tracking Under Occlusion and Varying Expression. Computer Vision and Pattern Recognition.14 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.