Daniel Racoceanu

7.4k total citations · 2 hit papers
97 papers, 2.8k citations indexed

About

Daniel Racoceanu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Biophysics. According to data from OpenAlex, Daniel Racoceanu has authored 97 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Artificial Intelligence, 37 papers in Computer Vision and Pattern Recognition and 23 papers in Biophysics. Recurrent topics in Daniel Racoceanu's work include AI in cancer detection (38 papers), Cell Image Analysis Techniques (23 papers) and Image Retrieval and Classification Techniques (16 papers). Daniel Racoceanu is often cited by papers focused on AI in cancer detection (38 papers), Cell Image Analysis Techniques (23 papers) and Image Retrieval and Classification Techniques (16 papers). Daniel Racoceanu collaborates with scholars based in France, Singapore and Peru. Daniel Racoceanu's co-authors include Humayun Irshad, Ludovic Roux, Antoine Veillard, Noureddine Zerhouni, Ryad Zemouri, Monjoy Saha, Nicolas Loménie, Chandan Chakraborty, Bassem Ben Cheikh and Purang Abolmaesumi and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and American Journal Of Pathology.

In The Last Decade

Daniel Racoceanu

90 papers receiving 2.7k citations

Hit Papers

Gland segmentation in colon histology images: The glas ch... 2014 2026 2018 2022 2016 2014 100 200 300 400 500
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.

  1. Gland segmentation in colon histology images: The glas challenge contest
    2016 570 citations Korsuk Sirinukunwattana, Josien P. W. Pluim et al. Medical Image Analysis profile →
  2. Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential
    2014 458 citations Daniel Racoceanu et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daniel Racoceanu France 21 1.8k 1.3k 1.0k 518 235 97 2.8k
Fuyong Xing United States 28 1.7k 0.9× 1.4k 1.1× 944 0.9× 681 1.3× 274 1.2× 79 2.9k
Hai Su United States 23 1.1k 0.6× 931 0.7× 544 0.5× 326 0.6× 151 0.6× 49 2.3k
Faisal Mahmood United States 24 1.9k 1.0× 683 0.5× 1.3k 1.3× 296 0.6× 428 1.8× 78 3.5k
Aurélio Campilho Portugal 28 1.0k 0.6× 1.7k 1.3× 2.3k 2.3× 199 0.4× 229 1.0× 140 3.7k
Laurent Heutte France 24 2.5k 1.4× 2.2k 1.7× 1.4k 1.4× 141 0.3× 148 0.6× 80 3.8k
Jinshan Tang United States 32 1.2k 0.6× 1.7k 1.3× 1.1k 1.0× 138 0.3× 220 0.9× 154 3.6k
Lin Yang United States 29 879 0.5× 1.4k 1.1× 463 0.4× 250 0.5× 486 2.1× 120 2.8k
Hongmin Cai China 27 912 0.5× 788 0.6× 906 0.9× 101 0.2× 398 1.7× 200 3.0k

Countries citing papers authored by Daniel Racoceanu

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Racoceanu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Racoceanu

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Racoceanu. A scholar is included among the top collaborators of Daniel Racoceanu 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 Daniel Racoceanu. Daniel Racoceanu 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
1.
Gilbert, Aubrey L., Pierre Lebranchu, Cristina Hobeanu, et al.. (2025). Artificial Intelligence‐Based Detection of Central Retinal Artery Occlusion Within 4.5 Hours on Standard Fundus Photographs. Journal of the American Heart Association. 14(13). e041441–e041441. 1 indexed citations
2.
3.
Boluda, Susana, Marie‐Claude Potier, Stéphane Haı̈k, et al.. (2024). Automated deep learning segmentation of neuritic plaques and neurofibrillary tangles in Alzheimer disease brain sections using a proprietary software. Journal of Neuropathology & Experimental Neurology. 83(9). 752–762. 2 indexed citations
4.
Hu, Wanming, et al.. (2024). Multi-scale feature fusion for prediction of IDH1 mutations in glioma histopathological images. Computer Methods and Programs in Biomedicine. 248. 108116–108116. 9 indexed citations
5.
6.
Hu, Wanming, R. Lambo, Zhicheng Zhang, et al.. (2021). Weakly Supervised Framework for Cancer Region Detection of Hepatocellular Carcinoma in Whole-Slide Pathologic Images Based on Multiscale Attention Convolutional Neural Network. American Journal Of Pathology. 192(3). 553–563. 16 indexed citations
7.
Fraggetta, Filippo, Vincenzo L’Imperio, Sabine Leh, et al.. (2021). Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP). Diagnostics. 11(11). 2167–2167. 69 indexed citations
8.
Martel, Anne L., Purang Abolmaesumi, Danail Stoyanov, et al.. (2020). Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Lecture notes in computer science. 7 indexed citations
9.
Martel, Anne L., Purang Abolmaesumi, Danail Stoyanov, et al.. (2020). Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Lecture notes in computer science. 127 indexed citations
10.
Racoceanu, Daniel, et al.. (2019). Deep Learning for Semantic Segmentation vs. Classification in Computational Pathology: Application to Mitosis Analysis in Breast Cancer Grading. Frontiers in Bioengineering and Biotechnology. 7. 145–145. 44 indexed citations
11.
Saha, Monjoy, Chandan Chakraborty, & Daniel Racoceanu. (2017). Efficient deep learning model for mitosis detection using breast histopathology images. Computerized Medical Imaging and Graphics. 64. 29–40. 147 indexed citations
12.
Racoceanu, Daniel & Frédérique Capron. (2016). Semantic Integrative Digital Pathology: Insights into Microsemiological Semantics and Image Analysis Scalability. Pathobiology. 83(2-3). 148–155. 4 indexed citations
13.
Sirinukunwattana, Korsuk, Josien P. W. Pluim, Hao Chen, et al.. (2016). Gland segmentation in colon histology images: The glas challenge contest. Medical Image Analysis. 35. 489–502. 570 indexed citations breakdown →
14.
Basu, Sreetama, et al.. (2013). A Stochastic Model for Automatic Extraction of 3D Neuronal Morphology. Lecture notes in computer science. 16(Pt 1). 396–403. 5 indexed citations
15.
Huang, Zhaohui, et al.. (2012). Online 3-D Tracking of Suspension Living Cells Imaged with Phase-Contrast Microscopy. IEEE Transactions on Biomedical Engineering. 59(7). 1924–1933. 8 indexed citations
16.
Depeursinge, Adrien, Daniel Racoceanu, Jimison Iavindrasana, et al.. (2010). Fusing visual and clinical information for lung tissue classification in high-resolution computed tomography. Artificial Intelligence in Medicine. 50(1). 13–21. 83 indexed citations
17.
Li, Hao, et al.. (2009). Nuclear pleomorphism scoring by selective cell nuclei detection. 35 indexed citations
18.
Brézillon, Patrick & Daniel Racoceanu. (2007). A Context Model for Content Based Medical Image Retrieval. 25(5). 327–332.
19.
20.
Racoceanu, Daniel, et al.. (1994). A singular perturbation approach to modeling and resolution of Markov chains. Systems Analysis Modelling Simulation. 15(2). 83–101. 1 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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