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.
Automated pulmonary nodule detection in CT images using deep convolutional neural networks
2018330 citationsHongtao Xie, Dongbao Yang et al.Pattern Recognitionprofile →
This map shows the geographic impact of Nannan Sun'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 Nannan Sun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nannan Sun more than expected).
This network shows the impact of papers produced by Nannan Sun. 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 Nannan Sun. The network helps show where Nannan Sun may publish in the future.
Co-authorship network of co-authors of Nannan Sun
This figure shows the co-authorship network connecting the top 25 collaborators of Nannan Sun.
A scholar is included among the top collaborators of Nannan Sun 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 Nannan Sun. Nannan Sun is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
2022·Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval·(unknown),
Jiahao Cao,
Nannan Sun,
Lei Jiang
6
4
Automated pulmonary nodule detection in CT images using deep convolutional neural networks breakdown →
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.