Maria Antonietta Pascali

Remind Me of Something? Zero-Shot Learning for Trustworthy Image Comparison in Rolling Stock

Oscar Papini, Giulio Del Corso, Davide Bulotta, Andrea Carboni et autres

it (code pays fourni par la source)

A Survey on Good AI: User-Centric AI Design in Healthcare

Andrea Berti, Valentina Giannini, Simone Mazzetti, Maria Antonietta Pascali et autres

it (code pays fourni par la source)

Topological Machine Learning for Raman Spectroscopy: Perspectives for Pancreatic Diseases

Francesco Conti, Gianmarco Lazzini, Raffaele Gaeta, Luca Emanuele Pollina et autres

The analysis of tissue samples from 17 subjects clinically diagnosed with chronic pancreatitis, ductal adenocarcinoma, or classified as controls has been collected and analyzed by Raman spectroscopy (RS). Such data are classified using a recent methodology which combines machine learning with advanced …

it, fr (code pays fourni par la source)

Topological Machine Learning for Discriminative Spectral Band Identification in Raman Spectroscopy of Pathological Samples

Francesco Conti, Davide Moroni, Maria Antonietta Pascali

In the field of Raman spectroscopy (RS), particularly when working with biological samples, identifying the chemical compounds most involved in specific pathologies is of critical importance for pathologists. The correlation between chemical substances present in biological tissue and pathology can contribute not …

it, fr (code pays fourni par la source)

SI-Lab Annual Research Report 2024

Muhammad Ch Awais, Anthony Baiamonte, Antonio Benassi, Andre Berti et autres

The Signal & Images Laboratory (SI-Lab) is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy …

NAVIGATOR: A regional multimodal imaging biobank initiative powered by AI tools for precision medicine in oncology

Gayanè Aghakhanyan, Andrea Barucci, Maria Antonietta Pascali, Massimiliano Assante et autres

it (code pays fourni par la source)

Fractal dimensions of complex networks: advocating for a topological approach

Rayna Andreeva, Haydeé Contreras-Peruyero, Sanjukta Krishnagopal, Nina Otter et autres

Topological Data Analysis (TDA) uses insights from topology to create representations of data able to capture global and local geometric and topological properties. Its methods have successfully been used to develop estimations of fractal dimensions for metric spaces that have been shown …

Image Mining: Current Problems in Theory and Applications

Igor B. Gurevich, Davide Moroni, Maria Antonietta Pascali, Bernd Radig et autres

ru, it, de (code pays fourni par la source)

AI Models in Cancer Diagnosis and Prognosis

Dimitris Filos, Stavros Sykiotis, Ioannis N. Tzortzis, Ioannis Rallis et autres

gr, pt, rs, fi, it (code pays fourni par la source)

Leveraging AI for Signal and Image Analysis in Medicine and Health

Marco Cafiso, Andrea Carboni, Claudia Caudai, Sara Colantonio et autres

The integration of artificial intelligence (AI) into the medical domain is driving innovation and progress in healthcare. This paper summarizes the research activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies …

Image Mining: Current Trends in Theory and Applications

Igor B. Gurevich, Davide Moroni, Maria Antonietta Pascali, V. V. Yashina

Abstract Image mining is the most promising and complex scientific direction of image analysis, dedicated to extracting knowledge and information from images, necessary for interpreting and understanding images and making intelligent decisions regarding objects, processes, events and phenomena presented in the image. …

ru, it (code pays fourni par la source)

Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences

Eugenia Mylona, Dimitrios I. Zaridis, Charalampos Kalantzopoulos, Nikolaos S. Tachos et autres

OBJECTIVES: Radiomics-based analyses encompass multiple steps, leading to ambiguity regarding the optimal approaches for enhancing model performance. This study compares the effect of several feature selection methods, machine learning (ML) classifiers, and sources of radiomic features, on models' performance for the diagnosis …

gr, it, pt, me, nl, es, tr, lt, gb, us (code pays fourni par la source)