Diana Mateus

2.8k total citations
63 papers, 1.5k citations indexed

About

Diana Mateus is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Diana Mateus has authored 63 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Radiology, Nuclear Medicine and Imaging, 24 papers in Computer Vision and Pattern Recognition and 18 papers in Artificial Intelligence. Recurrent topics in Diana Mateus's work include Radiomics and Machine Learning in Medical Imaging (23 papers), AI in cancer detection (15 papers) and Medical Imaging Techniques and Applications (8 papers). Diana Mateus is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (23 papers), AI in cancer detection (15 papers) and Medical Imaging Techniques and Applications (8 papers). Diana Mateus collaborates with scholars based in France, Germany and United Kingdom. Diana Mateus's co-authors include Nassir Navab, Loren Schwarz, Anne L. Martel, Danail Stoyanov, Purang Abolmaesumi, Daniel Racoceanu, María A. Zuluaga, Gustavo Carneiro, Marco Loog and Andrew P. Bradley and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Medical Imaging and Pattern Recognition.

In The Last Decade

Diana Mateus

59 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Diana Mateus France 20 683 492 431 259 146 63 1.5k
Martin Urschler Austria 22 890 1.3× 789 1.6× 652 1.5× 476 1.8× 206 1.4× 87 2.4k
Jiancheng Yang China 17 473 0.7× 613 1.2× 530 1.2× 312 1.2× 74 0.5× 62 1.6k
Vasileios Belagiannis Germany 18 1.1k 1.6× 533 1.1× 815 1.9× 225 0.9× 60 0.4× 49 2.1k
Liansheng Wang China 25 723 1.1× 561 1.1× 606 1.4× 316 1.2× 162 1.1× 112 1.7k
Martin Lillholm Denmark 17 281 0.4× 479 1.0× 572 1.3× 170 0.7× 79 0.5× 49 1.4k
Ching‐Wei Wang Taiwan 23 333 0.5× 402 0.8× 567 1.3× 326 1.3× 80 0.5× 82 1.8k
José Dolz Canada 23 826 1.2× 720 1.5× 618 1.4× 213 0.8× 107 0.7× 69 1.7k
Yueming Jin Hong Kong 19 724 1.1× 558 1.1× 333 0.8× 549 2.1× 536 3.7× 53 1.8k
Qianni Zhang United Kingdom 18 748 1.1× 418 0.8× 447 1.0× 161 0.6× 67 0.5× 97 1.5k

Countries citing papers authored by Diana Mateus

Since Specialization
Citations

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

Fields of papers citing papers by Diana Mateus

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Diana Mateus

This figure shows the co-authorship network connecting the top 25 collaborators of Diana Mateus. A scholar is included among the top collaborators of Diana Mateus 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 Diana Mateus. Diana Mateus 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.
2.
Bailly, Clément, Caroline Bodet‐Milin, Emmanuel Itti, et al.. (2024). PET-based lesion graphs meet clinical data: An interpretable cross-attention framework for DLBCL treatment response prediction. Computerized Medical Imaging and Graphics. 120. 102481–102481. 2 indexed citations
3.
Lacourpaille, Lilian, et al.. (2024). Ultrasound segmentation analysis via distinct and completed anatomical borders. International Journal of Computer Assisted Radiology and Surgery. 19(7). 1419–1427. 3 indexed citations
4.
Carlier, Thomas, Diana Mateus, Françoise Kraeber‐Bodéré, et al.. (2023). Prognostic Value of18F-FDG PET Radiomics Features at Baseline in PET-Guided Consolidation Strategy in Diffuse Large B-Cell Lymphoma: A Machine-Learning Analysis from the GAINED Study. Journal of Nuclear Medicine. 65(1). 156–162. 9 indexed citations
5.
Ballester, Miguel Á. González, et al.. (2022). Memory-aware curriculum federated learning for breast cancer classification. Computer Methods and Programs in Biomedicine. 229. 107318–107318. 72 indexed citations
6.
Mateus, Diana, Sonja Kirchhoff, Chlodwig Kirchhoff, et al.. (2021). Curriculum learning for improved femur fracture classification: Scheduling data with prior knowledge and uncertainty. Medical Image Analysis. 75. 102273–102273. 19 indexed citations
7.
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
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. 127 indexed citations
9.
Jamet, Bastien, Cristina Nanni, Clément Bailly, et al.. (2020). Random survival forest to predict transplant-eligible newly diagnosed multiple myeloma outcome including FDG-PET radiomics: a combined analysis of two independent prospective European trials. European Journal of Nuclear Medicine and Molecular Imaging. 48(4). 1005–1015. 44 indexed citations
10.
Kazi, Anees, Shadi Albarqouni, Chlodwig Kirchhoff, et al.. (2020). Precise proximal femur fracture classification for interactive training and surgical planning. International Journal of Computer Assisted Radiology and Surgery. 15(5). 847–857. 34 indexed citations
11.
Carlier, Thomas, Bastien Jamet, Clément Bailly, et al.. (2019). Leveraging RSF and PET images for prognosis of multiple myeloma at diagnosis. International Journal of Computer Assisted Radiology and Surgery. 15(1). 129–139. 25 indexed citations
12.
Haller, Bernhard, Diana Mateus, Thierry Ponchon, et al.. (2018). Optical classification of neoplastic colorectal polyps – a computer-assisted approach (the COACH study). Scandinavian Journal of Gastroenterology. 53(9). 1100–1106. 34 indexed citations
13.
Gutiérrez-Becker, Benjamín, Diana Mateus, Loïc Peter, & Nassir Navab. (2017). Guiding multimodal registration with learned optimization updates. Medical Image Analysis. 41. 2–17. 15 indexed citations
14.
Cosı́o, Fernando Arámbula, et al.. (2017). Spatial Compounding of 3-D Fetal Brain Ultrasound Using Probabilistic Maps. Ultrasound in Medicine & Biology. 44(1). 278–291. 8 indexed citations
15.
Peter, Loïc, Diana Mateus, Pierre Chatelain, et al.. (2016). Assisting the examination of large histopathological slides with adaptive forests. Medical Image Analysis. 35. 655–668. 4 indexed citations
16.
Rieke, Nicola, Christoph Hennersperger, Diana Mateus, & Nassir Navab. (2014). Ultrasound interactive segmentation with tensor-graph methods. HAL (Le Centre pour la Communication Scientifique Directe). 18. 690–693. 1 indexed citations
17.
Gutiérrez, Benjamín, et al.. (2014). A sparse approach to build shape models with routine clinical data. HAL (Le Centre pour la Communication Scientifique Directe). 258–261. 1 indexed citations
18.
Atasoy, Selen, Diana Mateus, Alexander Meining, Guang‐Zhong Yang, & Nassir Navab. (2011). Targeted Optical Biopsies for Surveillance Endoscopies. Lecture notes in computer science. 14(Pt 3). 83–90. 6 indexed citations
19.
Pauly, Olivier, Diana Mateus, & Nassir Navab. (2010). ImageCLEF 2010 Working Notes on the Modality Classification Subtask.. 7(1). 22–4. 1 indexed citations
20.
Atasoy, Selen, Ben Glocker, Stamatia Giannarou, et al.. (2009). Probabilistic Region Matching in Narrow-Band Endoscopy for Targeted Optical Biopsy. Lecture notes in computer science. 12(Pt 1). 499–506. 16 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Martin Urschler Breakdown of academic impact, for papers by Jiancheng Yang Breakdown of academic impact, for papers by Vasileios Belagiannis Breakdown of academic impact, for papers by Liansheng Wang Breakdown of academic impact, for papers by Martin Lillholm Breakdown of academic impact, for papers by Ching‐Wei Wang Breakdown of academic impact, for papers by José Dolz Breakdown of academic impact, for papers by Yueming Jin Breakdown of academic impact, for papers by Qianni Zhang
Rankless by CCL
2026