Leonardo Rundo

5.8k total citations · 2 hit papers
102 papers, 3.3k citations indexed

About

Leonardo Rundo is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Leonardo Rundo has authored 102 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Radiology, Nuclear Medicine and Imaging, 35 papers in Artificial Intelligence and 27 papers in Computer Vision and Pattern Recognition. Recurrent topics in Leonardo Rundo's work include Radiomics and Machine Learning in Medical Imaging (43 papers), AI in cancer detection (24 papers) and Advanced Neural Network Applications (12 papers). Leonardo Rundo is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (43 papers), AI in cancer detection (24 papers) and Advanced Neural Network Applications (12 papers). Leonardo Rundo collaborates with scholars based in Italy, United Kingdom and United States. Leonardo Rundo's co-authors include Evis Sala, Carmelo Militello, Giancarlo Mauri, Michael Yeung, Carola‐Bibiane Schönlieb, Salvatore Vitabile, Changhee Han, Hideki Nakayama, G. Russo and Andrea Tangherloni and has published in prestigious journals such as Scientific Reports, Expert Systems with Applications and IEEE Access.

In The Last Decade

Leonardo Rundo

100 papers receiving 3.2k 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.

AI applications to medical images: From machine learning to deep learning

2021 412 citations Leonardo Rundo et al. profile →
Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation

2021 363 citations Evis Sala, Carola‐Bibiane Schönlieb et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Leonardo Rundo Italy	33	1.4k	1.2k	1.0k	518	437	102	3.3k
Zafer Cömert Türkiye	31	1.4k 1.0×	1.5k 1.3×	858 0.8×	540 1.0×	459 1.1×	78	3.5k
Isabella Nogues United States	4	1.5k 1.1×	1.5k 1.3×	1.1k 1.1×	290 0.6×	442 1.0×	6	4.1k
Mingchen Gao United States	15	1.8k 1.3×	1.8k 1.6×	1.4k 1.4×	312 0.6×	532 1.2×	54	4.8k
Bernhard Kainz United Kingdom	24	1.5k 1.1×	1.1k 1.0×	1.5k 1.4×	262 0.5×	395 0.9×	91	4.0k
Rikiya Yamashita United States	18	1.6k 1.2×	1.1k 0.9×	638 0.6×	221 0.4×	390 0.9×	49	4.1k
Hoo-Chang Shin United States	10	1.9k 1.4×	2.0k 1.7×	1.4k 1.4×	381 0.7×	564 1.3×	10	5.0k
Mizuho Nishio Japan	24	1.8k 1.3×	1.0k 0.8×	665 0.6×	261 0.5×	725 1.7×	67	4.3k
Anne L. Martel Canada	30	1.9k 1.4×	1.4k 1.1×	978 0.9×	238 0.5×	717 1.6×	132	4.0k
Su Ruan France	33	2.0k 1.5×	1.4k 1.1×	2.0k 2.0×	842 1.6×	386 0.9×	171	5.0k
Daguang Xu United States	28	1.8k 1.3×	1.9k 1.6×	1.9k 1.8×	609 1.2×	349 0.8×	61	4.2k

Countries citing papers authored by Leonardo Rundo

Since Specialization

Citations

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

Fields of papers citing papers by Leonardo Rundo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Leonardo Rundo

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

All Works

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20 of 20 papers shown

Bartolotta, Tommaso Vincenzo, Carmelo Militello, Francesco Prinzi, et al.. (2024). Artificial intelligence-based, semi-automated segmentation for the extraction of ultrasound-derived radiomics features in breast cancer: a prospective multicenter study. La radiologia medica. 129(7). 977–988. 5 indexed citations

Manners, David Neil, Lorenzo Motta, Fulvio Zaccagna, et al.. (2024). Multiparametric MRI dataset for susceptibility-based radiomic feature extraction and analysis. Scientific Data. 11(1). 575–575. 5 indexed citations

Xie, Yuting, Fulvio Zaccagna, Leonardo Rundo, et al.. (2024). IMPA-Net: Interpretable Multi-Part Attention Network for Trustworthy Brain Tumor Classification from MRI. Diagnostics. 14(10). 997–997. 1 indexed citations

Abdar, Moloud, Farzad Masoudkabir, Leonardo Rundo, et al.. (2023). Binarized multi-gate mixture of Bayesian experts for cardiac syndrome X diagnosis: A clinician-in-the-loop scenario with a belief-uncertainty fusion paradigm. Information Fusion. 97. 101813–101813. 9 indexed citations

Militello, Carmelo, et al.. (2023). CT Radiomic Features and Clinical Biomarkers for Predicting Coronary Artery Disease. Cognitive Computation. 15(1). 238–253. 30 indexed citations

Reinius, Marika, Cathal McCague, Vlad Bura, et al.. (2023). Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study. Frontiers in Oncology. 13. 1085874–1085874. 2 indexed citations

Toia, Patrizia, Ludovico La Grutta, Salvatore Vitabile, et al.. (2023). Epicardial Adipose Tissue Changes during Statin Administration in Relation to the Body Mass Index: A Longitudinal Cardiac CT Study. Applied Sciences. 13(19). 10709–10709. 2 indexed citations

Grist, James T., Frank Riemer, Maria Lyasheva, et al.. (2022). Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI. Diagnostics. 12(9). 2125–2125. 13 indexed citations

Abdar, Moloud, Leonardo Rundo, Petia Radeva, et al.. (2022). Hercules: Deep Hierarchical Attentive Multilevel Fusion Model With Uncertainty Quantification for Medical Image Classification. IEEE Transactions on Industrial Informatics. 19(1). 274–285. 47 indexed citations

10.

Riva, Simone G., Paolo Cazzaniga, Marco S. Nobile, et al.. (2022). SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks. Symmetry. 14(1). 119–119. 7 indexed citations

11.

Militello, Carmelo, et al.. (2022). Robustness Analysis of DCE-MRI-Derived Radiomic Features in Breast Masses: Assessing Quantization Levels and Segmentation Agreement. Applied Sciences. 12(11). 5512–5512. 6 indexed citations

12.

Eijnatten, Maureen van, Leonardo Rundo, Kees Joost Batenburg, et al.. (2021). 3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning. Data Archiving and Networked Services (DANS). 10 indexed citations

13.

Militello, Carmelo, Andrea Ranieri, Leonardo Rundo, et al.. (2021). On Unsupervised Methods for Medical Image Segmentation: Investigating Classic Approaches in Breast Cancer DCE-MRI. Applied Sciences. 12(1). 162–162. 12 indexed citations

14.

Tangherloni, Andrea, Marco S. Nobile, Paolo Cazzaniga, et al.. (2021). FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks. PLoS Computational Biology. 17(9). e1009410–e1009410. 1 indexed citations

15.

Militello, Carmelo, Leonardo Rundo, Salvatore Vitabile, & Vincenzo Conti. (2021). Fingerprint Classification Based on Deep Learning Approaches: Experimental Findings and Comparisons. Symmetry. 13(5). 750–750. 39 indexed citations

16.

Le, Elizabeth, Nicholas R. Evans, Jason M. Tarkin, et al.. (2020). 105 Machine learning and carotid artery CT radiomics identify significant differences between culprit and non-culprit lesions in patients with stroke and transient ischaemic attack. A82.2–A84.

17.

Conti, Vincenzo, Leonardo Rundo, Carmelo Militello, et al.. (2020). A multimodal retina‐iris biometric system using the Levenshtein distance for spatial feature comparison. IET Biometrics. 10(1). 44–64. 17 indexed citations

18.

Militello, Carmelo, Leonardo Rundo, Luigi Minafra, et al.. (2020). MF2C3: Multi-Feature Fuzzy Clustering to Enhance Cell Colony Detection in Automated Clonogenic Assay Evaluation. Symmetry. 12(5). 773–773. 10 indexed citations

19.

Conti, Vincenzo, Carmelo Militello, Leonardo Rundo, & Salvatore Vitabile. (2020). A Novel Bio-Inspired Approach for High-Performance Management in Service-Oriented Networks. IEEE Transactions on Emerging Topics in Computing. 9(4). 1709–1722. 36 indexed citations

20.

Conti, Vincenzo, et al.. (2018). Energy Efficiency Evaluation of Dynamic Partial Reconfiguration in Field Programmable Gate Arrays: An Experimental Case Study. Energies. 11(4). 739–739. 3 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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