Remco Duits

69 papers receiving 1.1k citations

Hit Papers

Robust Retinal Vessel Segmentation via Locally Adaptive D...2016202620192022201650100150200250

Peers

Remco Duits
Comparison fields: 5 of 80
  • Radiology, Nuclear Medicine and Imaging 572
  • Computer Vision and Pattern Recognition 557
  • Ophthalmology 280
  • Applied Mathematics 120
  • Cognitive Neuroscience 106
Replace Luc Florack with:
Luc Florack Netherlands
Mikaël Rousson United States
Karl Krissian Spain
Chunhong Cao China
Rongjie Lai United States
Tanja Teuber Germany
V. Caselles Spain
Tomeu Coll Spain
Isabelle E. Magnin France
J.F. Boyce United Kingdom
Remco Duits relative to Luc Florack Netherlands Luc Florack's profile →
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Luc Florack · 1×
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Countries citing papers authored by Remco Duits

Since Specialization
Citations

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

Fields of papers citing papers by Remco Duits

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Remco Duits

This figure shows the co-authorship network connecting the top 25 collaborators of Remco Duits. A scholar is included among the top collaborators of Remco Duits 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 Remco Duits. Remco Duits is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
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Automated Segmentation of Lesions in Ultrasound Using Semi-pixel-wise Cycle Generative Adversarial Nets.
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6 34
7 9
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Fast implementations of contextual PDE's for HARDI data processing in DIPY
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11 17
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Cardiac motion estimation using covariant derivatives and Helmholtz decomposition
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Diffusion on the 3D Euclidean motion group for enhancement of HARDI data
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Crossing-preserving coherence-enhancing diffusion on invertible orientation scores
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Image processing via shift-twist invariant operations on orientation bundle functions
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About Remco Duits

Remco Duits is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging, having authored 72 papers that have together received 1.1k indexed citations. Recurring topics across this work include Advanced Neuroimaging Techniques and Applications (21 papers), Medical Image Segmentation Techniques (20 papers) and Image and Signal Denoising Methods (13 papers). The work is most often cited by research in Ophthalmology (280 citations), Computer Vision and Pattern Recognition (557 citations) and Radiology, Nuclear Medicine and Imaging (572 citations). Remco Duits has collaborated with scholars based in Netherlands, France and Russia. Frequent co-authors include Bart M. ter Haar Romeny, Erik Franken, Erik J. Bekkers, Jiong Zhang, Behdad Dashtbozorg, Josien P. W. Pluim, Luc Florack, Michael Felsberg, Gösta H. Granlund and Francesco Rossi. Their work appears in journals such as PLoS ONE, IEEE Transactions on Pattern Analysis and Machine Intelligence and 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.

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