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Francesca Ieva - IEEE Xplore Author Profile
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Francesca Ieva

Also published under: F. Ieva

Publications
6
Citations
6
Publications by Year
20212023
Co-Authors:
Show All Co-Authors (27)
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Francesca Ieva

Also published under: F. Ieva

Affiliation

Department of Mathematics
Politecnico di Milano
Milan, Italy

Publication Topics

Biography

Francesca Ieva was born in Milan, Italy, in 1984. She received the M.S. degree in mathematical engineering, in 2008, and the Ph.D. degree in mathematical models and methods for engineering, in 2012. She is currently an Associate Professor of statistics with the Department of Mathematics, Politecnico di Milano. She is also the Associate Head of the Center for Health Data Science, Human Technopole. She is the principal investigator of three national project grants and a number of funded projects. She is also on the Advisory Board of the Center for Healthcare Research and Pharmacoepidemiology. Her mentoring activity is comprehensive of more than 20 M.D. students and six Ph.D. students. Her research interests include health analytics and statis...
Publications
6
Citations
6
Publications by Year
20212023
Co-Authors:
Show All Co-Authors (27)
Author's Published Works

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A tissue-aware simulation framework for [18F]FLT spatiotemporal uptake in pancreatic ductal adenocarcinoma

Lara Cavinato;Jimin Hong;Stefan Reinhard;Martin Wartenberg;Paolo Zunino;Andrea Manzoni;Francesca Ieva;Kuangyu Shi

2023 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Positron Emission Tomography (PET)-based molecular imaging represents one of the most powerful tools to measure tumor biology variations and tumor microenvironment heterogeneity, which are major factors impacting cancer treatment failure. In this respect, Spatiotemporal Distribution Models based on Partial Differential Equations can provide an informative picture of in vivo cancer biology, ultimat...Show More

A tissue-aware simulation framework for [18F]FLT spatiotemporal uptake in pancreatic ductal adenocarcinoma

Lara Cavinato;Jimin Hong;Stefan Reinhard;Martin Wartenberg;Paolo Zunino;Andrea Manzoni;Francesca Ieva;Kuangyu Shi

Year: 2023 | Conference Paper |

Ask Your Data—Supporting Data Science Processes by Combining AutoML and Conversational Interfaces

Sara Pidó;Pietro Pinoli;Pietro Crovari;Francesca Ieva;Franca Garzotto;Stefano Ceri

IEEE Access
Year: 2023 | Volume: 11 | Journal Article |
Cited by: Papers (3)
Data Science is increasingly applied for solving real-life problems, in industry and in academic research, but mastering Data Science requires an interdisciplinary education that is still scarce on the market. Thus, there is a growing need for user-friendly tools that allow domain experts to directly apply data analysis methods to their datasets, without involving a Data Science expert. In this sc...Show More

Ask Your Data—Supporting Data Science Processes by Combining AutoML and Conversational Interfaces

Sara Pidó;Pietro Pinoli;Pietro Crovari;Francesca Ieva;Franca Garzotto;Stefano Ceri

Year: 2023 | Volume: 11 | Journal Article |

Distant supervision for imaging-based cancer sub-typing in Intrahepatic Cholangiocarcinoma

M.S. Savino;L. Cavinato;G. Costa;F. Fiz;G. Torzilli;L. Viganò;F. Ieva

2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Year: 2022 | Conference Paper |
Finding effective ways to perform cancer sub-typing is currently a trending research topic for therapy opti-mization and personalized medicine. Stemming from genomic field, several algorithms have been proposed. In the context of texture analysis, limited efforts have been attempted, yet imaging information is known to entail useful knowledge for clinical practice. We propose a distant supervision...Show More

Distant supervision for imaging-based cancer sub-typing in Intrahepatic Cholangiocarcinoma

M.S. Savino;L. Cavinato;G. Costa;F. Fiz;G. Torzilli;L. Viganò;F. Ieva

Year: 2022 | Conference Paper |

Recurrence-specific supervised graph clustering for subtyping Hodgkin Lymphoma radiomic phenotypes

L. Cavinato;N. Gozzi;M. Sollini;C. Carlo-Stella;A. Chiti;F. Ieva

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
The prediction at baseline of patients at high risk for therapy failure or recurrence would significantly impact on Hodgkin Lymphoma patients treatment, informing clinical practice. Current literature is extensively searching insights in radiomics, a promising framework for high-throughput imaging feature extraction, to derive biomarkers and quantitative prognostic factors from images. However, ex...Show More

Recurrence-specific supervised graph clustering for subtyping Hodgkin Lymphoma radiomic phenotypes

L. Cavinato;N. Gozzi;M. Sollini;C. Carlo-Stella;A. Chiti;F. Ieva

Year: 2021 | Conference Paper |

A Functional Data Analysis Approach to Left Ventricular Remodeling Assessment

Letizia Clementi;Caterina Gregorio;Laura Savaré;Francesca Ieva;Marco D. Santambrogio;Laura M. Sangalli

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Left ventricular remodeling is a mechanism common to various cardiovascular diseases affecting myocardial morphology. It can be often overlooked in clinical practice since the parameters routinely employed in the diagnostic process (e.g., the ejection fraction) mainly focus on evaluating volumetric aspects. Nevertheless, the integration of a quantitative assessment of structural modifications can ...Show More

A Functional Data Analysis Approach to Left Ventricular Remodeling Assessment

Letizia Clementi;Caterina Gregorio;Laura Savaré;Francesca Ieva;Marco D. Santambrogio;Laura M. Sangalli

Year: 2021 | Conference Paper |

Learning Signal Representations for EEG Cross-Subject Channel Selection and Trial Classification

Michela C. Massi;Francesca Ieva

2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)
Year: 2021 | Conference Paper |
EEG is a non-invasive powerful system that finds applications in several domains and research areas. Most EEG systems are multi-channel in nature, but multiple channels might include noisy and redundant information and increase computational times of automated EEG decoding algorithms. To reduce the signal-to-noise ratio, improve accuracy and reduce computational time, one may combine channel selec...Show More

Learning Signal Representations for EEG Cross-Subject Channel Selection and Trial Classification

Michela C. Massi;Francesca Ieva

Year: 2021 | Conference Paper |

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