Tom Doel

16 papers receiving 1.6k citations

Tom Doel's Hit Papers

NiftyNet: a deep-learning platform for medical imaging 2018 · 363 citations
3630+2+5Years since publication100200300400500

Peers

Tom Doel
Comparison fields: 5 of 131
  • Health Informatics 55
  • Computer Vision and Pattern Recognition 681
  • Radiology, Nuclear Medicine and Imaging 668
  • Neurology 229
  • Artificial Intelligence 504
Replace Michaël Aertsen with:
Michaël Aertsen Belgium
Jie‐Zhi Cheng China
Óscar Cámara Spain
Vishwesh Nath United States
Fahmi Khalifa United States
Wenjia Bai United Kingdom
Rosalind Pratt United Kingdom
Xiaohuan Cao China
Yipeng Hu United Kingdom
Tom Doel relative to Michaël Aertsen Belgium Michaël Aertsen's profile →
Citations per field
00.5×1.5×2.3×
Michaël Aertsen · 1×
Citations per year

Countries citing papers authored by Tom Doel

Since Specialization
Citations

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

Fields of papers citing papers by Tom Doel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Tom Doel, 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 Tom Doel Line = papers co-authored together Tom Doel links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1
Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning
Hit paper breakdown →
2018565
2
NiftyNet: a deep-learning platform for medical imaging
Hit paper breakdown →
2018363
3
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Hit paper breakdown →
2018295
4 2019143
5 201454
6 201653
7 201449
8 201646
9 201646
10 201227
11 20186
12 20154
13 20251
14 20171
15 20141
16 20171

About Tom Doel

Tom Doel is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging, Pediatrics, Perinatology and Child Health, Computer Vision and Pattern Recognition and Atomic and Molecular Physics, and Optics, having authored 16 papers that have together received 1.7k indexed citations. Recurring topics across this work include Lung Cancer Diagnosis and Treatment (5 papers), Fetal and Pediatric Neurological Disorders (4 papers), Atomic and Subatomic Physics Research (3 papers), Medical Image Segmentation Techniques (3 papers), Advanced Neural Network Applications (3 papers), Advanced Radiotherapy Techniques (2 papers), Chronic Obstructive Pulmonary Disease (COPD) Research (2 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). The work is most often cited by research in Health Informatics (55 citations), Computer Vision and Pattern Recognition (681 citations), Radiology, Nuclear Medicine and Imaging (668 citations), Neurology (229 citations) and Artificial Intelligence (504 citations). Tom Doel has collaborated with scholars based in United Kingdom, Belgium and Türkiye. Frequent co-authors include Sébastien Ourselin, Tom Vercauteren, Guotai Wang, Wenqi Li, Michaël Aertsen, Anna L. David, Jan Deprest, Premal A. Patel, Rosalind Pratt and María A. Zuluaga. Their work appears in journals such as International Journal of Radiation Oncology*Biology*Physics, Computer Methods and Programs in Biomedicine, IEEE Transactions on Medical Imaging, Radiotherapy and Oncology and Radiology.

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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