Antoine Veillard

1.0k total citations · 1 hit paper
19 papers, 681 citations indexed

About

Antoine Veillard is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Biophysics. According to data from OpenAlex, Antoine Veillard has authored 19 papers receiving a total of 681 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computer Vision and Pattern Recognition, 9 papers in Artificial Intelligence and 3 papers in Biophysics. Recurrent topics in Antoine Veillard's work include Image Retrieval and Classification Techniques (10 papers), Advanced Image and Video Retrieval Techniques (7 papers) and AI in cancer detection (6 papers). Antoine Veillard is often cited by papers focused on Image Retrieval and Classification Techniques (10 papers), Advanced Image and Video Retrieval Techniques (7 papers) and AI in cancer detection (6 papers). Antoine Veillard collaborates with scholars based in Singapore, France and China. Antoine Veillard's co-authors include Daniel Racoceanu, Ludovic Roux, Humayun Irshad, Vijay Chandrasekhar, Jie Lin, Hanlin Goh, Nicolas Loménie, Zhaohui Huang, Ling‐Yu Duan and Stéphane Bressan and has published in prestigious journals such as Signal Processing, Computerized Medical Imaging and Graphics and IEEE Reviews in Biomedical Engineering.

In The Last Decade

Antoine Veillard

19 papers receiving 658 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential

2014 458 citations Humayun Irshad, Antoine Veillard et al. IEEE Reviews in Biomedical Engineering profile →

Peers

Antoine Veillard

AI

CVPR

BIOPH

RNMI

MB

James Monaco United States

Nicolas Loménie France

Çiğdem Gündüz-Demir Türkiye

Akif Burak Tosun United States

Neeraj Kumar India

David Tellez Netherlands

Ju Han United States

J Vidal Spain

John Tomaszeweski United States

Neslihan Bayramoğlu Finland

James Monaco United States

Antoine Veillard

524 ×1.1

AI

345 ×0.8

CVPR

138 ×0.7

BIOPH

244 ×1.2

RNMI

138 ×2.3

MB

Citations per year, relative to Antoine Veillard Antoine Veillard (= 1×) peers James Monaco

Countries citing papers authored by Antoine Veillard

Since Specialization

Citations

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

Fields of papers citing papers by Antoine Veillard

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Antoine Veillard

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

All Works

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19 of 19 papers shown

1.

Veillard, Antoine, et al.. (2018). Fast 3D Seismic Interpretation with Unsupervised Deep Learning: Application to a Potash Network in the North Sea. Proceedings. 5 indexed citations

2.

Lin, Jie, et al.. (2017). DeepHash for Image Instance Retrieval. 133–141. 16 indexed citations

3.

Lin, Jie, et al.. (2017). Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval. 260–268. 13 indexed citations

4.

Chandrasekhar, Vijay, Jie Lin, Qianli Liao, et al.. (2017). Compression of Deep Neural Networks for Image Instance Retrieval. abs 1603 4595. 300–309. 12 indexed citations

5.

Chandrasekhar, Vijay, et al.. (2016). A practical guide to CNNs and Fisher Vectors for image instance retrieval. Signal Processing. 128. 426–439. 55 indexed citations

6.

Chandrasekhar, Vijay, et al.. (2016). Notice of Removal Co-sparsity regularized deep hashing for image instance retrieval. 2450–2454. 1 indexed citations

7.

Lin, Jie, et al.. (2016). Tiny Descriptors for Image Retrieval with Unsupervised Triplet Hashing. 397–406. 7 indexed citations

8.

Chandrasekhar, Vijay, et al.. (2015). Compact Global Descriptors for Visual Search. 333–342. 8 indexed citations

9.

Goh, Hanlin, et al.. (2015). Co-regularized deep representations for video summarization. 9 indexed citations

10.

Irshad, Humayun, Antoine Veillard, Ludovic Roux, & Daniel Racoceanu. (2014). Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential. IEEE Reviews in Biomedical Engineering. 7. 97–114. 458 indexed citations breakdown →

11.

Veillard, Antoine, et al.. (2013). Cell nuclei extraction from breast cancer histopathologyimages using colour, texture, scale and shape information. Diagnostic Pathology. 8(S1). 11 indexed citations

12.

Veillard, Antoine, et al.. (2012). Nuclei extraction from histopathological images using a marked point process approach. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8314. 831428–831428. 14 indexed citations

13.

Veillard, Antoine, Stéphane Bressan, & Daniel Racoceanu. (2012). SVM-based Framework for the Robust Extraction of Objects from Histopathological Images Using Color, Texture, Scale and Geometry. National University of Singapore. 53. 70–75. 6 indexed citations

14.

Veillard, Antoine, Daniel Racoceanu, & Stéphane Bressan. (2012). pRBF Kernels: A Framework for the Incorporation of Task-Specific Properties into Support Vector Methods. National University of Singapore. 2. 156–161. 1 indexed citations

15.

Veillard, Antoine, Daniel Racoceanu, & Stéphane Bressan. (2011). Incorporating Prior-Knowledge in Support Vector Machines by Kernel Adaptation. National University of Singapore. 5. 591–596. 3 indexed citations

16.

Huang, Zhaohui, et al.. (2010). Time-efficient sparse analysis of histopathological whole slide images. Computerized Medical Imaging and Graphics. 35(7-8). 579–591. 46 indexed citations

17.

Veillard, Antoine, Nicolas Loménie, & Daniel Racoceanu. (2010). An Exploration Scheme for Large Images: Application to Breast Cancer Grading. 6. 3472–3475. 9 indexed citations

18.

Veillard, Antoine, et al.. (2010). Support Vector Methods for Sentence Level Machine Translation Evaluation. National University of Singapore. 347–348. 1 indexed citations

19.

Roux, Ludovic, et al.. (2009). A cognitive virtual microscopic framework for knowlege-based exploration of large microscopic images in breast cancer histopathology. PubMed. 2009. 3697–3702. 6 indexed citations

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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