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Valentina Giannini
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2020 – today
- 2024
- [j6]Yanhua Zhang
, Gabriella Balestra
MultiTrans: Multi-branch transformer network for medical image segmentation. Comput. Methods Programs Biomed. 254: 108280 (2024) - [c26]Debora Cafaro, Giovanni Cappello, Roberto Cannella, Tommaso Vincenzo Bartolotta, Giovanni Grignani, Alessandra Merlini, Daniele Regge, Valentina Giannini:
Bag-of-Words Algorithm to Predict Response to Treatment in Gist Tumors Using CT Scans. ISBI 2024: 1-5 - [c25]Sara Colantonio, Andrea Berti, Gianluca Carloni, Claudia Caudai, Giulio Del Corso, Danila Germanese, Eva Pachetti, Maria Antonietta Pascali, Varvara Kalokyri, Haridimos Kondylakis, Charalampos Kalantzopoulos, Nikolaos S. Tachos, Dimitris I. Fotiadis, Valentina Giannini, Simone Mazzetti, Daniele Regge, Nickolas Papanikolaou, Konstantinos Marias, Manolis Tsiknakis:
Data and System Traceability for Transparent AI in Medical Imaging. SEBD 2024: 615-623 - 2023
- [c24]Giulia Nicoletti, Caterina Marchiò, Samanta Rosati, Enrico Berrino, Maria Costanza Aquilano, Emanuela Bonoldi, Gabriella Balestra, Daniele Regge, Valentina Giannini:
How Do Norms and Noise Impact Clustering Results? A Robustness Analysis Applied to Digital Pathology. BIBE 2023: 333-337 - [c23]Jovana Panic, Arianna Defeudis, Daniele Regge, Gabriella Balestra, Samanta Rosati, Valentina Giannini:
Comparison between Different Approaches for the Creation of the Training Set: How Clustering and Dimensionality Impact the Performance of a Deep Learning Model. BIBE 2023: 393-396 - 2022
- [j5]Massimo Salvi
Integration of Deep Learning and Active Shape Models for More Accurate Prostate Segmentation in 3D MR Images. J. Imaging 8(5): 133 (2022) - [c22]Jovana Panic
A fully automatic deep learning algorithm to segment rectal Cancer on MR images: a multi-center study. EMBC 2022: 5066-5069 - [c21]Arianna Defeudis
A Deep Learning model to segment liver metastases on CT images acquired at different time-points during chemotherapy. MeMeA 2022: 1-6 - [c20]Giovanni Maimone, Giulia Nicoletti
Comparison of Machine and Deep Learning models for automatic segmentation of prostate cancers on multiparametric MRI. MeMeA 2022: 1-5 - [c19]Jovana Panic
Impact of network parameters on a U-Net based system for rectal cancer segmentation on MR images. MeMeA 2022: 1-6 - 2021
- [c18]Arianna Defeudis
Comparison of radiomics approaches to predict resistance to 1st line chemotherapy in liver metastatic colorectal cancer. EMBC 2021: 3305-3308 - [c17]Davide Barra, Giulia Nicoletti
Deep learning model for automatic prostate segmentation on bicentric T2w images with and without endorectal coil. EMBC 2021: 3370-3373 - [c16]Giulia Nicoletti
Virtual biopsy in prostate cancer: can machine learning distinguish low and high aggressive tumors on MRI? EMBC 2021: 3374-3377 - 2020
- [c15]Valentina Giannini
An innovative radiomics approach to predict response to chemotherapy of liver metastases based on CT images. EMBC 2020: 1339-1342 - [c14]Bruno De Santi
Comparison of Histogram-based Textural Features between Cancerous and Normal Prostatic Tissue in Multiparametric Magnetic Resonance Images. EMBC 2020: 1671-1674 - [c13]Jovana Panic
A Convolutional Neural Network based system for Colorectal cancer segmentation on MRI images. EMBC 2020: 1675-1678 - [c12]Valentina Giannini
Deep learning to segment liver metastases on CT images: impact on a radiomics method to predict response to chemotherapy. MeMeA 2020: 1-5
2010 – 2019
- 2019
- [c11]Bruno De Santi
Multimodal T2w and DWI Prostate Gland Automated Registration. EMBC 2019: 4427-4430 - 2018
- [c10]Valentina Giannini
Radiomics for pretreatment prediction of pathological response to neoadjuvant therapy using magnetic resonance imaging: Influence of feature selection. ISBI 2018: 285-288 - [c9]Samanta Rosati
Correlation based Feature Selection impact on the classification of breast cancer patients response to neoadjuvant chemotherapy. MeMeA 2018: 1-5 - 2016
- [j4]Valentina Giannini
MR-T2-weighted signal intensity: a new imaging biomarker of prostate cancer aggressiveness. Comput. methods Biomech. Biomed. Eng. Imaging Vis. 4(3-4): 130-134 (2016) - [c8]Samanta Rosati
Dataset homogeneity assessment for a prostate cancer CAD system. MeMeA 2016: 1-7 - 2015
- [j3]Valentina Giannini
A fully automatic computer aided diagnosis system for peripheral zone prostate cancer detection using multi-parametric magnetic resonance imaging. Comput. Medical Imaging Graph. 46: 219-226 (2015) - [c7]Francesco Rossi, Alessandro Savino
A 3D Voxel Neighborhood Classification Approach within a Multiparametric MRI Classifier for Prostate Cancer Detection. IWBBIO (1) 2015: 231-239 - [c6]Samanta Rosati
ChiMerge discretization method: Impact on a computer aided diagnosis system for prostate cancer in MRI. MeMeA 2015: 297-302 - 2014
- [j2]Anna Vignati
A new algorithm for automatic vascular mapping of DCE-MRI of the breast: Clinical application of a potential new biomarker. Comput. Methods Programs Biomed. 117(3): 482-488 (2014) - [c5]Alessandro Savino
A Prostate Cancer Computer Aided Diagnosis Software including Malignancy Tumor Probabilistic Classification. BIOIMAGING 2014: 49-54 - 2013
- [j1]Carlo Giupponi, Silvio Giove
A dynamic assessment tool for exploring and communicating vulnerability to floods and climate change. Environ. Model. Softw. 44: 136-147 (2013) - [c4]Valentina Giannini
A prostate CAD system based on multiparametric analysis of DCE T1-w, and DW automatically registered images. Computer-Aided Diagnosis 2013: 86703E - 2012
- [b1]Valentina Giannini:
Computer Aided Diagnosis systems for MR cancer detection. Polytechnic University of Turin, Italy, 2012 - 2011
- [c3]Massimo De Luca, Valentina Giannini
A fully automatic method to register the prostate gland on T2-weighted and EPI-DWI images. EMBC 2011: 8029-8032 - [c2]Simone Mazzetti
A CAD system based on multi-parametric analysis for cancer prostate detection on DCE-MRI. Computer-Aided Diagnosis 2011: 79633Q
2000 – 2009
- 2009
- [c1]Anna Vignati
A fully automatic lesion detection method for DCE-MRI fat-suppressed breast images. Computer-Aided Diagnosis 2009: 726026
Coauthor Index
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