Xiao Da
- Radiology, Nuclear Medicine and Imaging top 2%
- Artificial Intelligence top 5%
- Genetics top 2%
- Physiology top 10%
- Psychiatry and Mental health top 5%
- Co-authors
- Christos DavatzikosMehdi MirzaAaron CourvilleYoshua BengioIan GoodfellowHamed AkbariMichel BilelloYangming Ou
- Topics
- Glioma Diagnosis and Treatment (8 papers)Radiomics and Machine Learning in Medical Imaging (8 papers)MRI in cancer diagnosis (7 papers)
- Journals
- Nature CommunicationsPLoS ONEBrain
- Partner nations
- United StatesChinaNorway
In The Last Decade
Xiao Da
50 papers receiving 1.7k citations
Hit Papers
Peers
Comparison fields: 5 of 133
- Radiology, Nuclear Medicine and Imaging 558
- Artificial Intelligence 466
- Genetics 351
- Physiology 316
- Psychiatry and Mental health 312
Countries citing papers authored by Xiao Da
This map shows the geographic impact of Xiao Da'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 Xiao Da with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiao Da more than expected).
Fields of papers citing papers by Xiao Da
This network shows the impact of papers produced by Xiao Da. 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 Xiao Da. The network helps show where Xiao Da may publish in the future.
Co-authorship network of co-authors of Xiao Da
This figure shows the co-authorship network connecting the top 25 collaborators of Xiao Da. A scholar is included among the top collaborators of Xiao Da 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 Xiao Da. Xiao Da is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 18 | |
| 3 | 10 | |
| 4 | 6 | |
| 5 | 1 | |
| 6 | 7 | |
| 7 | Improving the Universality and Learnability of Neural Programmer-Interpreters with Combinator Abstraction | 3 |
| 8 | 21 | |
| 9 | Provable Data Possession System for Realistic Cloud Storage Environments | 1 |
| 10 | 48 | |
| 11 | 107 | |
| 12 | 31 | |
| 13 | An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networksbreakdown → | 341 |
| 14 | 63 | |
| 15 | 75 | |
| 16 | Effective method for analysis of Cisco IOS image injection attack | 1 |
| 17 | 35 | |
| 18 | 98 | |
| 19 | 136 | |
| 20 | A case study of acquisition of ba construction in Mandarin | 1 |
About Xiao Da
Xiao Da is a scholar working on Software, Genetics and Radiology, Nuclear Medicine and Imaging, having authored 51 papers that have together received 1.7k indexed citations. Recurring topics across this work include Glioma Diagnosis and Treatment (8 papers), Radiomics and Machine Learning in Medical Imaging (8 papers) and MRI in cancer diagnosis (7 papers). The work is most often cited by research in Genetics (351 citations), Health Informatics (33 citations) and Radiology, Nuclear Medicine and Imaging (558 citations). Xiao Da has collaborated with scholars based in United States, China and Norway. Frequent co-authors include Christos Davatzikos, Mehdi Mirza, Aaron Courville, Yoshua Bengio, Ian Goodfellow, Hamed Akbari, Michel Bilello, Yangming Ou, Donald M. O’Rourke and Ronald L. Wolf. Their work appears in journals such as Nature Communications, PLoS ONE and Brain.
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.