Oliver Díaz

1.7k total citations
72 papers, 970 citations indexed

About

Oliver Díaz is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Artificial Intelligence. According to data from OpenAlex, Oliver Díaz has authored 72 papers receiving a total of 970 indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Radiology, Nuclear Medicine and Imaging, 33 papers in Pulmonary and Respiratory Medicine and 33 papers in Artificial Intelligence. Recurrent topics in Oliver Díaz's work include AI in cancer detection (33 papers), Digital Radiography and Breast Imaging (30 papers) and Medical Imaging Techniques and Applications (27 papers). Oliver Díaz is often cited by papers focused on AI in cancer detection (33 papers), Digital Radiography and Breast Imaging (30 papers) and Medical Imaging Techniques and Applications (27 papers). Oliver Díaz collaborates with scholars based in Spain, United Kingdom and Netherlands. Oliver Díaz's co-authors include Robert Martí, Xavier Lladó, Moi Hoon Yap, David R. Dance, Kenneth C. Young, Kevin Wells, Karim Lekadir, Ioannis Sechopoulos, Kaisar Kushibar and Alejandro Rodríguez‐Ruiz and has published in prestigious journals such as Gastroenterology, PLoS ONE and IEEE Transactions on Medical Imaging.

In The Last Decade

Oliver Díaz

68 papers receiving 942 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Oliver Díaz Spain 16 719 538 345 162 137 72 970
Jonas Teuwen Netherlands 15 962 1.3× 671 1.2× 268 0.8× 216 1.3× 120 0.9× 62 1.4k
Guy Nir Canada 14 501 0.7× 438 0.8× 307 0.9× 253 1.6× 177 1.3× 26 966
Jihye Yun South Korea 15 564 0.8× 190 0.4× 323 0.9× 137 0.8× 110 0.8× 36 985
Matiullah Naqibullah Denmark 6 735 1.0× 275 0.5× 551 1.6× 112 0.7× 128 0.9× 10 982
Sujeeth Bharadwaj United States 4 830 1.2× 425 0.8× 481 1.4× 169 1.0× 73 0.5× 6 1.2k
Roman Zeleznik United States 8 716 1.0× 271 0.5× 399 1.2× 184 1.1× 32 0.2× 15 892
Saša Grbić United States 16 496 0.7× 174 0.3× 205 0.6× 177 1.1× 183 1.3× 31 857
Margarita Chevalier Spain 11 630 0.9× 568 1.1× 355 1.0× 134 0.8× 35 0.3× 59 905
Alexandra Edwards United States 15 533 0.7× 484 0.9× 226 0.7× 70 0.4× 95 0.7× 33 797
Kyu-Hwan Jung South Korea 18 679 0.9× 264 0.5× 215 0.6× 141 0.9× 226 1.6× 38 1.1k

Countries citing papers authored by Oliver Díaz

Since Specialization
Citations

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

Fields of papers citing papers by Oliver Díaz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Oliver Díaz

This figure shows the co-authorship network connecting the top 25 collaborators of Oliver Díaz. A scholar is included among the top collaborators of Oliver Díaz 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 Oliver Díaz. Oliver Díaz 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.
Koçak, Burak, Ilaria Ambrosini, Tugba Akinci D’Antonoli, et al.. (2025). Explanation and Elaboration with Examples for METRICS (METRICS-E3): an initiative from the EuSoMII Radiomics Auditing Group. Insights into Imaging. 16(1). 175–175. 1 indexed citations
2.
Koçak, Burak, Michail E. Klontzas, Arnaldo Stanzione, et al.. (2025). Evaluation metrics in medical imaging AI: fundamentals, pitfalls, misapplications, and recommendations. 3. 100030–100030. 6 indexed citations
3.
García‐Tornel, Álvaro, Manuel Requena, Jiahui Li, et al.. (2024). Deep learning-based model for difficult transfemoral access prediction compared with human assessment in stroke thrombectomy. Journal of NeuroInterventional Surgery. 17(6). 653–659.
4.
Garbarino, Sara, et al.. (2024). A study on the role of radiomics feature stability in predicting breast cancer subtypes. CINECA IRIS Institutial Research Information System (University of Genoa). 30. 75–75. 1 indexed citations
5.
Kilintzis, Vassilis, Haridimos Kondylakis, Katerina Nikiforaki, et al.. (2024). Public data homogenization for AI model development in breast cancer. European Radiology Experimental. 8(1). 42–42. 5 indexed citations
6.
Gkontra, Polyxeni, Marina Camacho, Oliver Díaz, et al.. (2023). Cardiometabolic risk estimation using exposome data and machine learning. International Journal of Medical Informatics. 179. 105209–105209. 15 indexed citations
7.
Camacho, Marina, Oliver Díaz, Maria Bulgheroni, et al.. (2023). Cardiometabolic Risk Estimation Using Exposome Data and Machine Learning. SSRN Electronic Journal. 1 indexed citations
8.
Kushibar, Kaisar, Richard Osuala, Oliver Díaz, et al.. (2023). High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection. Frontiers in Oncology. 12. 1044496–1044496. 16 indexed citations
9.
Pawaskar, Manjiri, et al.. (2022). Impact of universal varicella vaccination on the use and cost of antibiotics and antivirals for varicella management in the United States. PLoS ONE. 17(6). e0269916–e0269916. 9 indexed citations
10.
Osuala, Richard, Kaisar Kushibar, Akis Linardos, et al.. (2022). Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging. Medical Image Analysis. 84. 102704–102704. 41 indexed citations
11.
Osuala, Richard, Kaisar Kushibar, Akis Linardos, et al.. (2021). A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions.. Spiral (Imperial College London). 8 indexed citations
12.
Zanca, Federica, Irene Hernández‐Girón, Michele Avanzo, et al.. (2021). Expanding the medical physicist curricular and professional programme to include Artificial Intelligence. Physica Medica. 83. 174–183. 28 indexed citations
13.
Díaz, Oliver, et al.. (2021). CoLe-CNN+: Context learning - Convolutional neural network for COVID-19-Ground-Glass-Opacities detection and segmentation. Computers in Biology and Medicine. 136. 104689–104689. 12 indexed citations
14.
Marsden, Philip A., et al.. (2020). A semi-empirical model for scatter field reduction in digital mammography. Physics in Medicine and Biology. 66(4). 45001–45001. 3 indexed citations
15.
Díaz, Oliver, et al.. (2020). Deep learning for mass detection in Full Field Digital Mammograms. Computers in Biology and Medicine. 121. 103774–103774. 92 indexed citations
16.
Elangovan, Premkumar, Alistair Mackenzie, Lucy M. Warren, et al.. (2019). Validation of modelling tools for simulating wide-angle DBT systems. 85–85. 2 indexed citations
17.
Fedon, Christian, Marco Caballo, Oliver Díaz, et al.. (2018). Monte Carlo study on optimal breast voxel resolution for dosimetry estimates in digital breast tomosynthesis. Physics in Medicine and Biology. 64(1). 15003–15003. 6 indexed citations
18.
Díaz, Oliver, Robert Martí, Yago Díez, et al.. (2017). Local breast density assessment using reacquired mammographic images. European Journal of Radiology. 93. 121–127. 8 indexed citations
19.
Bouwman, Ramona W., Oliver Díaz, Ruben E. van Engen, et al.. (2013). Phantoms for quality control procedures in digital breast tomosynthesis: dose assessment. Physics in Medicine and Biology. 58(13). 4423–4438. 14 indexed citations
20.
Gutierrez, Daniel Rodriguez, et al.. (2009). Partial volume effects in dynamic contrast magnetic resonance renal studies. European Journal of Radiology. 75(2). 221–229. 11 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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