activity
20182021
most citedRadiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma

1 citations · 2 across the 4 of their papers we have counts for

collaborators

7 papers

cs.LG20211 cited

Score-oriented loss (SOL) functions

Francesco Marchetti, Sabrina Guastavino, Michele Piana +1

Loss functions engineering and the assessment of forecasting performances are two crucial and intertwined aspects of supervised machine learning. This paper focuses on binary class…

q-bio.TO20201 cited

Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma

Daniela Schenonea, Rita Lai, Michele Cea +13

Multiple Myeloma (MM) is a blood cancer implying bone marrow involvement, renal damages and osteolytic lesions. The skeleton involvement of MM is at the core of the present paper,…

q-bio.TO2019

Automated Definition of Skeletal Disease Burden in Metastatic Prostate Carcinoma: a 3D analysis of SPECT/CT images

Francesco Fiz, Helmut Dittmann, Cristina Campi +7

To meet the current need for skeletal tumor-load estimation in prostate cancer (mCRPC), we developed a novel approach, based on adaptive bone segmentation. In this study, we compar…

astro-ph.SR2019

Feature ranking of active region source properties in solar flare forecasting and the uncompromised stochasticity of flare occurrence

Cristina Campi, Federico Benvenuto, Anna Maria Massone +3

Solar flares originate from magnetically active regions but not all solar active regions give rise to a flare. Therefore, the challenge of solar flare prediction benefits by an int…

cs.CV2019

Geometry of the Hough transforms with applications to synthetic data

Mauro C. Beltrametti, Cristina Campi, Anna Maria Massone +1

In the framework of the Hough transform technique to detect curves in images, we provide a bound for the number of Hough transforms to be considered for a successful optimization o…

astro-ph.SR2018

Flare forecasting and feature ranking using SDO/HMI data

Michele Piana, Cristina Campi, Federico Benvenuto +2

We describe here the application of a machine learning method for flare forecasting using vectors of properties extracted from images provided by the Helioseismic and Magnetic Imag…