Tom Doel

18 papers receiving 1.9k citations

Tom Doel's Hit Papers

Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning 2018 · 631 citations
6310+2+5Years since publication200400600

Peers

Tom Doel
Comparison fields: 5 of 127
  • Health Informatics 52
  • Computer Vision and Pattern Recognition 741
  • Radiology, Nuclear Medicine and Imaging 667
  • Neurology 234
  • Artificial Intelligence 543
Replace Michaël Aertsen with:
Michaël Aertsen Belgium
Martin Rajchl Canada
Jie‐Zhi Cheng China
Marius George Linguraru United States
Óscar Cámara Spain
Premal A. Patel United Kingdom
Rosalind Pratt United Kingdom
Xiaohuan Cao China
Ashnil Kumar Australia
Tina Kapur United States
Tom Doel relative to Michaël Aertsen Belgium Michaël Aertsen's profile →
Citations per field
00.5×1.5×1.9×
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

18 of 18 papers shown
#Work
1
Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning
Hit paper breakdown →
2018631
2
NiftyNet: a deep-learning platform for medical imaging
Hit paper breakdown →
2018415
3
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Hit paper breakdown →
2018341
4 2019180
5 201459
6 201658
7 201455
8 201652
9 201649
10 201230
11 201829
12 201612
13 20186
14 20154
15 20251
16 20141
17 20171
18 20171

About Tom Doel

Tom Doel is a scholar working on Pulmonary and Respiratory Medicine, Pediatrics, Perinatology and Child Health, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 18 papers that have together received 1.9k indexed citations. Recurring topics across this work include Fetal and Pediatric Neurological Disorders (5 papers), Advanced Neural Network Applications (4 papers), Lung Cancer Diagnosis and Treatment (4 papers), Domain Adaptation and Few-Shot Learning (4 papers), Medical Image Segmentation Techniques (3 papers), Atomic and Subatomic Physics Research (3 papers), Effects of Radiation Exposure (2 papers) and Chronic Obstructive Pulmonary Disease (COPD) Research (2 papers). The work is most often cited by research in Health Informatics (52 citations), Computer Vision and Pattern Recognition (741 citations), Radiology, Nuclear Medicine and Imaging (667 citations), Neurology (234 citations) and Artificial Intelligence (543 citations). Tom Doel has collaborated with scholars based in United Kingdom, Belgium and Portugal. Frequent co-authors include Sébastien Ourselin, Tom Vercauteren, Guotai Wang, Michaël Aertsen, Anna L. David, Jan Deprest, Wenqi Li, Premal A. Patel, Rosalind Pratt and María A. Zuluaga. Their work appears in journals such as Computer Methods and Programs in Biomedicine, International Journal of Radiation Oncology*Biology*Physics, IEEE Transactions on Medical Imaging, Computerized Medical Imaging and Graphics 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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