Yingda Xia

3.2k citations
26 papers · 919 · h-index 13

Impact in

Papers in

Yingda Xia

24 papers receiving 904 citations

Peers

Yingda Xia
Comparison fields: 5 of 72
  • Computer Vision and Pattern Recognition 504
  • Radiology, Nuclear Medicine and Imaging 393
  • Artificial Intelligence 458
  • Neurology 81
  • Health Informatics 11
Replace Zhuotun Zhu with:
Zhuotun Zhu United States
Hyunseok Seo South Korea
Shuyue Guan United States
Leandro Alves Neves Brazil
Shanhui Sun United States
Marcelo Zanchetta do Nascimento Brazil
Zhennan Yan United States
Cheng Bian China
Jintai Chen China
Yingda Xia relative to Zhuotun Zhu United States Zhuotun Zhu's profile →
Citations per field
00.5×1.5×
Zhuotun Zhu · 1×
Citations per year

Countries citing papers authored by Yingda Xia

Since Specialization
Citations

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

Fields of papers citing papers by Yingda Xia

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Yingda Xia, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Yingda Xia Line = papers co-authored together Yingda Xia links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020191
2 2022127
3 2020113
4 2018112
5 202094
6 201973
7 201864
8 202122
9 202321
10
A 3D Coarse-to-Fine Framework for Automatic Pancreas Segmentation.
201719
11 201916
12 202014
13 201912
14 20247
15 20227
16 20226
17 20234
18 20244
19
Thickened 2D Networks for 3D Medical Image Segmentation.
20193
20 20233

About Yingda Xia

Yingda Xia is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Oncology and Biomedical Engineering, having authored 26 papers that have together received 919 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (17 papers), Radiomics and Machine Learning in Medical Imaging (10 papers), Medical Image Segmentation Techniques (10 papers), AI in cancer detection (8 papers), COVID-19 diagnosis using AI (6 papers), Pancreatic and Hepatic Oncology Research (4 papers), Medical Imaging and Analysis (3 papers) and Domain Adaptation and Few-Shot Learning (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (504 citations), Radiology, Nuclear Medicine and Imaging (393 citations), Artificial Intelligence (458 citations), Neurology (81 citations) and Health Informatics (11 citations). Yingda Xia has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Alan Yuille, Zhuotun Zhu, Fengze Liu, Elliot K. Fishman, Wei Shen, Dong Yang, Daguang Xu, Jinzheng Cai, Lequan Yu and Holger R. Roth. Their work appears in journals such as Lecture notes in computer science, Medical Image Analysis, IEEE Transactions on Neural Networks and Learning Systems, Nature Communications and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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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