Leonardo Rundo

Tenure-Track Assistant Professor (RTD-b)

Università degli Studi di Napoli Federico II

Italy

Research Interests

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About

Dr. Leonardo Rundo is a Tenure-Track Assistant Professor (RTD-b) at the Università degli Studi di Salerno, Italy. His research focuses on the application of artificial intelligence in medical imaging and computational biology, as demonstrated by his recent articles. Key areas of interest include personalized seizure detection, brain tumor classification using MRI, and the integration of AI tools in clinical research.

Articles (14)

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2025

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2024

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2024

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2023

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2023

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2023

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2023

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2023

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2022

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2022

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2022

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2022

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2022

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2022

Collaborators (16)

David Manners

University of Bologna

ITALY

Leonardo Rundo

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Articles (14)

Personalizing Seizure Detection for Individual Patients by Optimal Selection of EEG Signals

IMPA-Net: Interpretable Multi-Part Attention Network for Trustworthy Brain Tumor Classification from MRI

Image biomarkers and explainable AI: handcrafted features versus deep learned features

Integrating Artificial Intelligence Tools in the Clinical Research Setting: The Ovarian Cancer Use Case

Hercules: Deep Hierarchical Attentive Multilevel Fusion Model With Uncertainty Quantification for Medical Image Classification

Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation

Epicardial and thoracic subcutaneous fat texture analysis in patients undergoing cardiac CT

Epicardial Adipose Tissue Changes during Statin Administration in Relation to the Body Mass Index: A Longitudinal Cardiac CT Study

Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI

SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks

Artificial Intelligence Applied to Medical Imaging and Computational Biology

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey

Robustness Analysis of DCE-MRI-Derived Radiomic Features in Breast Masses: Assessing Quantization Levels and Segmentation Agreement

Collaborators (16)

David Manners

Petia Radeva

Tommaso Vincenzo Bartolotta

Massimo Galia

Fulvio Zaccagna

Martin John Graves

Alejandro Frangi

James Grist

Marco Nobile

ANDREA TANGHERLONI

Cesare Gagliardo

Ramona Woitek

Moloud Abdar

Alexander Jung

Martino Giaquinto

Cathal McCague

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