Valentina Giannini

发表

A prostate CAD system based on multiparametric analysis of DCE T1-w, and DW automatically registered images

Daniele Regge, Valentina Giannini, Anna Vignati, 2013, Medical Imaging.

A Convolutional Neural Network based system for Colorectal cancer segmentation on MRI images

Gabriella Balestra, Jovana Panic, Daniele Regge, 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

A Fully Automatic Multiscale 3-Dimensional Hessian-Based Algorithm for Vessel Detection in Breast DCE-MRI

Pasquale Borrelli, Daniele Regge, Valentina Giannini, 2012, Investigative radiology.

A fully automatic lesion detection method for DCE-MRI fat-suppressed breast images

Lia Morra, Daniele Regge, Valentina Giannini, 2009, Medical Imaging.

A fully automatic method to register the prostate gland on T2-weighted and EPI-DWI images

Francesco Porpiglia, Daniele Regge, Valentina Giannini, 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Dataset homogeneity assessment for a prostate cancer CAD system

Gabriella Balestra, Samanta Rosati, Cristina Castagneri, 2016, 2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

Specificity improvement of a CAD system for multiparametric MR prostate cancer using texture features and artificial neural networks

Gabriella Balestra, Samanta Rosati, Valentina Giannini, 2017 .

Correlation based Feature Selection impact on the classification of breast cancer patients response to neoadjuvant chemotherapy

Gabriella Balestra, Samanta Rosati, Daniele Regge, 2018, 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

A CAD system based on multi-parametric analysis for cancer prostate detection on DCE-MRI

C. Bracco, Daniele Regge, Valentina Giannini, 2011, Medical Imaging.

A dynamic assessment tool for exploring and communicating vulnerability to floods and climate change

Carlo Giupponi, Silvio Giove, Valentina Giannini, 2013, Environ. Model. Softw..

An innovative radiomics approach to predict response to chemotherapy of liver metastases based on CT images

Gabriella Balestra, Jovana Panic, Daniele Regge, 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

Radiomics for pretreatment prediction of pathological response to neoadjuvant therapy using magnetic resonance imaging: Influence of feature selection

Gabriella Balestra, Samanta Rosati, Cristina Castagneri, 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

MR-T2-weighted signal intensity: a new imaging biomarker of prostate cancer aggressiveness

Daniele Regge, Valentina Giannini, Anna Vignati, 2016, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

A new algorithm for automatic vascular mapping of DCE-MRI of the breast: Clinical application of a potential new biomarker

Daniele Regge, Valentina Giannini, Anna Vignati, 2014, Comput. Methods Programs Biomed..

Texture Features and Artificial Neural Networks: A Way to Improve the Specificity of a CAD System for Multiparametric MR Prostate Cancer

Gabriella Balestra, Samanta Rosati, Valentina Giannini, 2016 .

A Computer Aided Diagnosis System To Detect Prostate Cancer With Endorectal Multiparametric MRI

Alessandro Savino, Stefano Di Carlo, A. Benso, 2013 .

ChiMerge discretization method: Impact on a computer aided diagnosis system for prostate cancer in MRI

Gabriella Balestra, Samanta Rosati, Daniele Regge, 2015, 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings.

Comparison of Histogram-based Textural Features between Cancerous and Normal Prostatic Tissue in Multiparametric Magnetic Resonance Images

Kristen M. Meiburger, Filippo Molinari, Valentina Giannini, 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

A fully automatic computer aided diagnosis system for peripheral zone prostate cancer detection using multi-parametric magnetic resonance imaging

Francesco Porpiglia, Daniele Regge, Valentina Giannini, 2015, Comput. Medical Imaging Graph..

A 3D Voxel Neighborhood Classification Approach within a Multiparametric MRI Classifier for Prostate Cancer Detection

Alfredo Benso, Gianfranco Politano, Alessandro Savino, 2015, IWBBIO.

A Prostate Cancer Computer Aided Diagnosis Software including Malignancy Tumor Probabilistic Classification

Alfredo Benso, Gianfranco Politano, Alessandro Savino, 2014, BIOIMAGING.

Comparison of radiomics approaches to predict resistance to 1st line chemotherapy in liver metastatic colorectal cancer

Francesco Rizzetto, Daniele Regge, Valentina Giannini, 2021, 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

Big data in oncologic imaging

Michele Stasi, Daniele Regge, Valentina Giannini, 2017, La radiologia medica.

Impact of inter-reader contouring variability on textural radiomics of colorectal liver metastases

Alberto Torresin, Silvia Ghezzi, Francesco Rizzetto, 2020, European Radiology Experimental.

Computer Aided Diagnosis systems for MR cancer detection

Valentina Giannini, V. Giannini, 2012 .

Registration, lesion detection, and discrimination for breast dynamic contrast-enhanced magnetic resonance imaging

Filippo Molinari, Lia Morra, Daniele Regge, 2013 .

Multiparametric magnetic resonance imaging of the prostate with computer-aided detection: experienced observer performance study

Daniele Regge, Valentina Giannini, Simone Mazzetti, 2017, European Radiology.

A computer-aided diagnosis (CAD) scheme for pretreatment prediction of pathological response to neoadjuvant therapy using dynamic contrast-enhanced MRI texture features.

Daniele Regge, Valentina Giannini, Simone Mazzetti, 2017, The British journal of radiology.

A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies

Piergiorgio Cerello, Alberto Traverso, Michele Stasi, 2018, European Radiology.