A5053090175- Radiomics and Machine Learning in Medical Imaging
- AI in cancer detection
- COVID-19 diagnosis using AI
- Advanced X-ray and CT Imaging
- Digital Radiography and Breast Imaging
- Medical Imaging Techniques and Applications
- Artificial Intelligence in Healthcare and Education
- Lung Cancer Diagnosis and Treatment
- Infrared Thermography in Medicine
- Autism Spectrum Disorder Research
- Functional Brain Connectivity Studies
- Radiology practices and education
- Advanced Neuroimaging Techniques and Applications
- Advanced MRI Techniques and Applications
- Brain Tumor Detection and Classification
- COVID-19 Clinical Research Studies
- Enhanced Recovery After Surgery
- Neonatal and fetal brain pathology
- Surgical Simulation and Training
- Voice and Speech Disorders
- Sarcoma Diagnosis and Treatment
- Neurogenetic and Muscular Disorders Research
- Graphite, nuclear technology, radiation studies
- Machine Learning in Healthcare
- Medical Image Segmentation Techniques
Istituto Nazionale di Fisica Nucleare
2019-2025
Istituto Nazionale di Fisica Nucleare, Sezione di Pisa
2023-2024
Scuola Normale Superiore
2019-2022
University of Pisa
2019-2021
Abstract Background: The integration of the information encoded in multiparametric MRI images can enhance performance machine-learning classifiers. In this study, we investigate whether combination structural and functional might improve performances a deep learning (DL) model trained to discriminate subjects with Autism Spectrum Disorders (ASD) respect typically developing controls (TD). Material methods We analyzed both brain scans publicly available within ABIDE I II data collections....
Abstract Super-Resolution Microscopy (SRM) surpasses Abbe's diffraction limit, thus enabling nanoscale observation of cells. However, SRM techniques, such as Stochastic Optical Reconstruction (STORM), suffer from long acquisition times which can significantly impact imaging throughput. To address this issue, we adapted the Enhanced Generative Adversarial Network (ESRGAN) natural to microscopy images. Our goal is generate super-resolution images widefield in shorter times. We implemented for...
This article's main contributions are twofold: 1) to demonstrate how apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice domain of healthcare and 2) investigate research question what does "trustworthy AI" mean at time COVID-19 pandemic. To this end, we present results a post-hoc self-assessment evaluate trustworthiness an system predicting multiregional score conveying degree lung compromise patients, developed verified by...
Abstract Background The role of computed tomography (CT) in the diagnosis and characterization coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated performance a software for quantitative analysis chest CT, LungQuant system, by comparing its results with independent visual evaluations group 14 clinical experts. aim this work is to evaluate ability automated tool extract information from lung relevant design support model. Methods segments both lungs lesions...
This study aims at exploiting artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. The limited data availability annotation quality are relevant factors in training AI-methods. We investigated effects using multiple datasets, heterogeneously populated annotated according to different criteria.We developed an automated analysis pipeline, LungQuant system, based on a cascade two U-nets. first one (U-net[Formula: see text]) is...
We propose and evaluate a procedure for the explainability of breast density deep learning based classifier. A total 1662 mammography exams labeled according to BI-RADS categories was used. built residual Convolutional Neural Network, trained it studied responses model input changes, such as different distributions class labels in training test sets suitable image pre-processing. The aim identify steps analysis with relevant impact on classifier performance explainability. used grad-CAM...
Abstract Computed tomography (CT) scans are used to evaluate the severity of lung involvement in patients affected by COVID-19 pneumonia. Here, we present an improved version LungQuant automatic segmentation software ( v 2), which implements a cascade three deep neural networks (DNNs) segment lungs and lesions associated with The first network (BB-net) defines bounding box enclosing lungs, second one (U-net $$_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub>...
Abstract Objective . Radiomics is a promising valuable analysis tool consisting in extracting quantitative information from medical images. However, the extracted radiomics features are too sensitive to variations used image acquisition and reconstruction parameters. This limited robustness hinders generalizable validity of radiomics-assisted models. Our aim investigate possible harmonization strategy based on matching quality improve feature robustness. Approach. We acquired CT scans...
Magnetic Resonance (MR) parameters mapping in muscle Imaging (mMRI) is predominantly performed using pattern recognition-based algorithms, which are characterised by high computational costs and scalability issues the context of multi-parametric mapping. Deep Learning (DL) has been demonstrated to be a robust efficient method for rapid MR However, its application mMRI domain investigate Neuromuscular Disorders (NMDs) not yet explored. In addition, data-driven DL models suffered...
Lung Computed Tomography (CT) is an imaging technique useful to assess the severity of COVID-19 infection in symptomatic patients and monitor its evolution over time. CT can be analysed with support deep learning methods for both aforementioned tasks. We have developed a U-net based algorithm segment lesions. Unfortunately, public datasets populated huge amount labelled scans affected by are not available. In this work, we first review all currently available scans, presenting extensive...