Matthew C. H. Lee
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging
- Biomedical Engineering
- Pediatrics, Perinatology and Child Health
- Co-authors
- Konstantinos KamnitsasMartin RajchlDaniel RueckertMellisa DamodaramJoseph V. HajnalBernhard KainzOzan OktayWenjia Bai
- Topics
- Medical Image Segmentation Techniques (2 papers)Advanced Image and Video Retrieval Techniques (1 paper)Advanced Neural Network Applications (1 paper)
- Cited by
- Computer Vision and Pattern RecognitionHealth InformaticsRadiology, Nuclear Medicine and Imaging
- Journals
- IEEE Transactions on Medical Imaging
- Partner nations
- United Kingdom
In The Last Decade
Matthew C. H. Lee
2 papers receiving 284 citations
Peers
Comparison fields: 5 of 69
- Computer Vision and Pattern Recognition 167
- Artificial Intelligence 109
- Radiology, Nuclear Medicine and Imaging 107
- Biomedical Engineering 39
- Pediatrics, Perinatology and Child Health 27
Countries citing papers authored by Matthew C. H. Lee
This map shows the geographic impact of Matthew C. H. Lee'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 Matthew C. H. Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew C. H. Lee more than expected).
Fields of papers citing papers by Matthew C. H. Lee
This network shows the impact of papers produced by Matthew C. H. Lee. 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 Matthew C. H. Lee. The network helps show where Matthew C. H. Lee may publish in the future.
Co-authorship network of co-authors of Matthew C. H. Lee
This figure shows the co-authorship network connecting the top 25 collaborators of Matthew C. H. Lee. A scholar is included among the top collaborators of Matthew C. H. Lee 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 Matthew C. H. Lee. Matthew C. H. Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Unsupervised Lesion Detection in Brain CT using Bayesian Convolutional Autoencoders | 31 |
| 2 | 257 |
About Matthew C. H. Lee
Matthew C. H. Lee is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence, having authored 2 papers that have together received 288 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (2 papers), Advanced Image and Video Retrieval Techniques (1 paper) and Advanced Neural Network Applications (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (167 citations), Health Informatics (11 citations) and Radiology, Nuclear Medicine and Imaging (107 citations). Matthew C. H. Lee has collaborated with scholars based in United Kingdom. Frequent co-authors include Konstantinos Kamnitsas, Martin Rajchl, Daniel Rueckert, Mellisa Damodaram, Joseph V. Hajnal, Bernhard Kainz, Ozan Oktay, Wenjia Bai, Mary Rutherford and Jonathan Passerat‐Palmbach. Their work appears in journals such as IEEE Transactions on Medical Imaging.
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.