2026
Book
Open Access
Design manual for accessibility and the inclusive use of cultural heritage: from human functioning to the functioning of cultural places
Bernacchia Monica, Biocca Luigi, Buzzi Marina, Capirci Olga, Caponera Federica, Carella Giuseppina, Ceccarani Patrizia, Cetorelli Gabriella, Contardi Anna, Della Fina Valentina, Di Renzo Alessio, Filetici Maria Grazia, Galesi Giulio, Genta Chiara, Gervasio Carmine Fernando, Grassini Aldo, Guido Manuel Roberto, Leporini Barbara, Luongo Mariajosè, Maglorio Massimo, Marino Carmen, Mezzelani Alessandra, Montuschi Carla, Pagano Alfonsina, Papi Luca, Pennacchi Barbara, Pietroni Eva, Ricci Enrico, Rossini Mauro, Russo Francesco Paolo, Schiavone Elisabetta, Schivo Flavia, Scianna Andrea, Trasatti Annalisa, Zanut Stefano
The English-language version of the Manual, presented in an expanded edition in March 2026, represents an update of the volume with two appendices that are part of the institutional series of Policy Briefs of the Department of Human and Social Sciences, Cultural Heritage (DSU) of the National Research Council (CNR). This new edition, in addition to gathering the salient aspects of "universal design," starting from the concept of the functioning of individuals to the functioning of cultural places, presents additional current aspects through documents that summarize the results of research on relevant topics and their implications for policy and social debate. In the first addendum, the choice was to explore the issues of appropriate, respectful, and correct language regarding accessibility based on Legislative Decree 62/2024, centered on a "Person First" vision, as enshrined by the World Health Organization (2001) and the Convention on the Rights of Persons with Disabilities (2006). The second document proposes an integrated approach to accessible trails, understood as landscape experiences to be enjoyed independently and safely, capable of meeting the needs of each individual
Bernacchia Monica, Biocca Luigi, Buzzi Marina, Capirci Olga, Caponera Federica, Carella Giuseppina, Ceccarani Patrizia, Cetorelli Gabriella, Contardi Anna, Della Fina Valentina, Di Renzo Alessio, Filetici Maria Grazia, Galesi Giulio, Genta Chiara, Gervasio Carmine Fernando, Grassini Aldo, Guido Manuel Roberto, Leporini Barbara, Luongo Mariajosè, Maglorio Massimo, Marino Carmen, Mezzelani Alessandra, Montuschi Carla, Pagano Alfonsina, Papi Luca, Pennacchi Barbara, Pietroni Eva, Ricci Enrico, Rossini Mauro, Russo Francesco Paolo, Schiavone Elisabetta, Schivo Flavia, Scianna Andrea, Trasatti Annalisa, Zanut Stefano
The English-language version of the Manual, presented in an expanded edition in March 2026, represents an update of the volume with two appendices that are part of the institutional series of Policy Briefs of the Department of Human and Social Sciences, Cultural Heritage (DSU) of the National Research Council (CNR). This new edition, in addition to gathering the salient aspects of "universal design," starting from the concept of the functioning of individuals to the functioning of cultural places, presents additional current aspects through documents that summarize the results of research on relevant topics and their implications for policy and social debate. In the first addendum, the choice was to explore the issues of appropriate, respectful, and correct language regarding accessibility based on Legislative Decree 62/2024, centered on a "Person First" vision, as enshrined by the World Health Organization (2001) and the Convention on the Rights of Persons with Disabilities (2006). The second document proposes an integrated approach to accessible trails, understood as landscape experiences to be enjoyed independently and safely, capable of meeting the needs of each individual
See at:
CNR IRIS 
| www.dsu.cnr.it | CNR IRIS 
2026
Journal article
Closed Access
Blood Eosinophils Are Accurate Biomarkers for the Management of Eosinophilic Oesophagitis: Prospective, Multi‐Centre Study
Visaggi Pierfrancesco, Pellegatta Gaia, Siboni Stefano, Maniero Daria, Del Corso Giulio, Solinas Irene, Mitilini Mauro, Testi Federico, Cairoli Gaia, Dulmin Isabella, Poletti Valeria, Marcozzi Giacomo, Sozzi Marco, Piazza Lucia, Stefani Donati Delio, Bellini Massimo, Repici Alessandro, Savarino Edoardo Vincenzo, Bortoli Nicola De
Background & Aims: The diagnosis and follow-up of eosinophilic oesophagitis (EoE) currently rely on repeated upper endoscopies (EGD) with biopsies, which are invasive, resource-intensive and environmentally costly. Non-invasive biomarkers for EoE are needed. We investigated the role of blood eosinophils and lymphocytes in the management of EoE. Methods: This was a prospective study conducted at four EoE referral centres. Consecutive adults undergoing EGD with biopsies for known or suspected EoE were enrolled. Based on oesophageal peak eosinophil count (PEC) and clinical history, patients were divided into EoE (histologically active or in remission) and non-EoE dysphagia (NED). Prior to the EGD, a full blood count was obtained. Clinical, endoscopic and histologic findings were recorded. Receiver operating characteristic curve analysis was used to assess the predictive ability of blood biomarkers (AUC). Results: We enrolled 209 patients (123 EoE and 86 NED). For the diagnosis of EoE, an absolute eosinophil count (AEC) of 155 eosinophils/mm3 had an AUC of 85%. For the assessment of histological disease activity, an AEC of 325 eosinophils/mm3 had an AUC of 70.5% for the identification of histological remission following treatment. AEC showed a positive correlation with PEC on histology and the EoE endoscopic reference score with Spearman's Rho of 0.4 (p < 0.0001). Conclusion: Eosinophil absolute and relative counts in the peripheral blood could be used in the initial assessment of patients presenting with dysphagia to accurately differentiate EoE from NED and to predict histological remission of EoE.Source: ALIMENTARY PHARMACOLOGY & THERAPEUTICS, vol. 63 (issue 9), pp. 1256-1264
DOI: 10.1111/apt.70524
Metrics:
Visaggi Pierfrancesco, Pellegatta Gaia, Siboni Stefano, Maniero Daria, Del Corso Giulio, Solinas Irene, Mitilini Mauro, Testi Federico, Cairoli Gaia, Dulmin Isabella, Poletti Valeria, Marcozzi Giacomo, Sozzi Marco, Piazza Lucia, Stefani Donati Delio, Bellini Massimo, Repici Alessandro, Savarino Edoardo Vincenzo, Bortoli Nicola De
Background & Aims: The diagnosis and follow-up of eosinophilic oesophagitis (EoE) currently rely on repeated upper endoscopies (EGD) with biopsies, which are invasive, resource-intensive and environmentally costly. Non-invasive biomarkers for EoE are needed. We investigated the role of blood eosinophils and lymphocytes in the management of EoE. Methods: This was a prospective study conducted at four EoE referral centres. Consecutive adults undergoing EGD with biopsies for known or suspected EoE were enrolled. Based on oesophageal peak eosinophil count (PEC) and clinical history, patients were divided into EoE (histologically active or in remission) and non-EoE dysphagia (NED). Prior to the EGD, a full blood count was obtained. Clinical, endoscopic and histologic findings were recorded. Receiver operating characteristic curve analysis was used to assess the predictive ability of blood biomarkers (AUC). Results: We enrolled 209 patients (123 EoE and 86 NED). For the diagnosis of EoE, an absolute eosinophil count (AEC) of 155 eosinophils/mm3 had an AUC of 85%. For the assessment of histological disease activity, an AEC of 325 eosinophils/mm3 had an AUC of 70.5% for the identification of histological remission following treatment. AEC showed a positive correlation with PEC on histology and the EoE endoscopic reference score with Spearman's Rho of 0.4 (p < 0.0001). Conclusion: Eosinophil absolute and relative counts in the peripheral blood could be used in the initial assessment of patients presenting with dysphagia to accurately differentiate EoE from NED and to predict histological remission of EoE.Source: ALIMENTARY PHARMACOLOGY & THERAPEUTICS, vol. 63 (issue 9), pp. 1256-1264
DOI: 10.1111/apt.70524
Metrics:
See at:
Alimentary Pharmacology & Therapeutics | CNR IRIS | Alimentary Pharmacology & Therapeutics
2026
Other
Restricted
Spoke 9 - AGRITECH 36-MONTH REPORT
Pucci Laura, Tomassi Elena, Arouna Nafiou, Gabriele Morena, Peres Fabbri Laryssa, Pozzo Luisa, Conte Giuseppe, Cremonesi Paola, Castiglioni Bianca, Moroni Davide, Martinelli Massimo
This document represents the 36-month report on products of animal origin intended for human consumption.Project(s): Spoke 9 AGRITECH
Pucci Laura, Tomassi Elena, Arouna Nafiou, Gabriele Morena, Peres Fabbri Laryssa, Pozzo Luisa, Conte Giuseppe, Cremonesi Paola, Castiglioni Bianca, Moroni Davide, Martinelli Massimo
This document represents the 36-month report on products of animal origin intended for human consumption.Project(s): Spoke 9 AGRITECH
2026
Contribution to conference
Restricted
Blood pressure measurement in Italian mountain shelters: a world hypertension day initiative
Bilo Grzegorz, Zanotti Lucia, Croce Alessandro, D’angelo Carla, Faini Andrea, Martinelli Massimo, Muiesan Maria Lorenza, Pengo Martino F., Pratali Lorenza, Vega Deceno Jose Ivan, Soranna Davide, Zambon Antonella, Virdis Agostino, Strapazzon Giacomo, Agazzi Giancelso, Parati Gianfranco
World Hypertension Day and May Measurement Month are large-scale population initiatives aimed at increasing awareness of hypertension and providing opportunistic blood pressure (BP) screening. In Italy, “Blood Pressure Measurement in Mountain Shelters” has been conducted since 2016 as a joint initiative of Italian Society of Hypertension, Italian Alpine Club and Italian Society of Mountain Medicine, with the aim of providing advice and education on hypertension and cardiovascular risk among visitors of mountain shelters. Methods. Trained volunteers provided free advice to visitors of mountain shelters throughout Italy. Basic demographic, lifestyle and clinical information was collected with anonymous questionnaire. Seated BP was obtained using manual or oscillometric devices as the average of three measurements. The initiative took place in summer when mountain attendance is highest. The present analysis of merged data collected in 2019, 2022, 2023, 2024 and 2025 editions focuses on describing participant characteristics, including BP, stratified by altitude of data collection. Results. After exclusion of incomplete records (missing age, sex or BP), data of 12,317 participants from 140 mountain shelters were analysed. No relevant differences were observed across the different years in terms of participant characteristics or BP levels. Participant characteristics are reported in the Table. Median BP and body mass index were within normal limits, and the prevalence of diabetes, hypercholesterolaemia and hypertension was lower than that in the general Italian population. Fewer than 15% of participants reported taking at least one antihypertensive medication. Higher altitudes of data collection were associated with higher male sex prevalence and heart rate and with lower age, BMI, SpO2, prevalence of hypercholesterolemia and hypertension. Only minor differences were observed in measured BP. Conclusions. Blood Pressure Measurement in Mountain Shelters is a unique initiative, willingly attended by visitors to mountain areas. Participants generally displayed a favourable cardiovascular risk profile; however, a substantial proportion, including those assessed at higher altitudes, presented at least one cardiovascular risk factor. This screening and educational initiative represents a valuable opportunity to identify individuals at risk and to provide counselling on cardiovascular prevention among visitors of mountain shelters.
Bilo Grzegorz, Zanotti Lucia, Croce Alessandro, D’angelo Carla, Faini Andrea, Martinelli Massimo, Muiesan Maria Lorenza, Pengo Martino F., Pratali Lorenza, Vega Deceno Jose Ivan, Soranna Davide, Zambon Antonella, Virdis Agostino, Strapazzon Giacomo, Agazzi Giancelso, Parati Gianfranco
World Hypertension Day and May Measurement Month are large-scale population initiatives aimed at increasing awareness of hypertension and providing opportunistic blood pressure (BP) screening. In Italy, “Blood Pressure Measurement in Mountain Shelters” has been conducted since 2016 as a joint initiative of Italian Society of Hypertension, Italian Alpine Club and Italian Society of Mountain Medicine, with the aim of providing advice and education on hypertension and cardiovascular risk among visitors of mountain shelters. Methods. Trained volunteers provided free advice to visitors of mountain shelters throughout Italy. Basic demographic, lifestyle and clinical information was collected with anonymous questionnaire. Seated BP was obtained using manual or oscillometric devices as the average of three measurements. The initiative took place in summer when mountain attendance is highest. The present analysis of merged data collected in 2019, 2022, 2023, 2024 and 2025 editions focuses on describing participant characteristics, including BP, stratified by altitude of data collection. Results. After exclusion of incomplete records (missing age, sex or BP), data of 12,317 participants from 140 mountain shelters were analysed. No relevant differences were observed across the different years in terms of participant characteristics or BP levels. Participant characteristics are reported in the Table. Median BP and body mass index were within normal limits, and the prevalence of diabetes, hypercholesterolaemia and hypertension was lower than that in the general Italian population. Fewer than 15% of participants reported taking at least one antihypertensive medication. Higher altitudes of data collection were associated with higher male sex prevalence and heart rate and with lower age, BMI, SpO2, prevalence of hypercholesterolemia and hypertension. Only minor differences were observed in measured BP. Conclusions. Blood Pressure Measurement in Mountain Shelters is a unique initiative, willingly attended by visitors to mountain areas. Participants generally displayed a favourable cardiovascular risk profile; however, a substantial proportion, including those assessed at higher altitudes, presented at least one cardiovascular risk factor. This screening and educational initiative represents a valuable opportunity to identify individuals at risk and to provide counselling on cardiovascular prevention among visitors of mountain shelters.
2026
Journal article
Open Access
Research progress on Ocean observations technology and information systems
Tsabaris Christos, Pieri Gabriele
The oceans play a crucial role in the global ecosystem; they shape trends in the climate, weather, water management, and health (including biogeochemical cycles). Although innovative technologies have been developed for the marine environment to better understand the ocean’s processes, several constraints remain in ocean observation, hindering the replacement of laboratory and routine monitoring methods. TSource: JOURNAL OF MARINE SCIENCE AND ENGINEERING, vol. 14 (issue 9)
Project(s): New Approach to Underwater Technologies for Innovative, Low-cost Ocean obServation
Tsabaris Christos, Pieri Gabriele
The oceans play a crucial role in the global ecosystem; they shape trends in the climate, weather, water management, and health (including biogeochemical cycles). Although innovative technologies have been developed for the marine environment to better understand the ocean’s processes, several constraints remain in ocean observation, hindering the replacement of laboratory and routine monitoring methods. TSource: JOURNAL OF MARINE SCIENCE AND ENGINEERING, vol. 14 (issue 9)
Project(s): New Approach to Underwater Technologies for Innovative, Low-cost Ocean obServation
See at:
CNR IRIS | www.mdpi.com | CNR IRIS
2026
Journal article
Open Access
A classification-aware super-resolution framework for ship targets in SAR imagery
Awais Ch Muhammad, Reggiannini Marco, Moroni Davide, Karakus Oktay
High-resolution imagery plays a critical role in improving the performance of visual recognition tasks such as classification, detection, and segmentation. In many domains, including remote sensing and surveillance, low-resolution images can limit the accuracy of automated analysis. To address this, super-resolution (SR) techniques have been widely adopted to attempt to reconstruct high-resolution images from low-resolution inputs. Related traditional approaches focus solely on enhancing image quality based on pixel-level metrics, leaving the relationship between super-resolved image fidelity and downstream classification performance largely underexplored. This raises a key question: can integrating classification objectives directly into the super-resolution process further improve classification accuracy? In this paper, we try to respond to this question by investigating the relationship between super-resolution and classification through the deployment of a specialised algorithmic strategy. We propose a novel methodology that increases the resolution of synthetic aperture radar imagery by optimising loss functions that account for both image quality and classification performance. Our approach improves image quality, as measured by scientifically ascertained image quality indicators, while also enhancing classification accuracy.Source: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, vol. 19, pp. 6614-6622
DOI: 10.1109/jstars.2026.3655550
Metrics:
Awais Ch Muhammad, Reggiannini Marco, Moroni Davide, Karakus Oktay
High-resolution imagery plays a critical role in improving the performance of visual recognition tasks such as classification, detection, and segmentation. In many domains, including remote sensing and surveillance, low-resolution images can limit the accuracy of automated analysis. To address this, super-resolution (SR) techniques have been widely adopted to attempt to reconstruct high-resolution images from low-resolution inputs. Related traditional approaches focus solely on enhancing image quality based on pixel-level metrics, leaving the relationship between super-resolved image fidelity and downstream classification performance largely underexplored. This raises a key question: can integrating classification objectives directly into the super-resolution process further improve classification accuracy? In this paper, we try to respond to this question by investigating the relationship between super-resolution and classification through the deployment of a specialised algorithmic strategy. We propose a novel methodology that increases the resolution of synthetic aperture radar imagery by optimising loss functions that account for both image quality and classification performance. Our approach improves image quality, as measured by scientifically ascertained image quality indicators, while also enhancing classification accuracy.Source: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, vol. 19, pp. 6614-6622
DOI: 10.1109/jstars.2026.3655550
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS
2026
Journal article
Open Access
Stem education and ICT-enhanced tools for students with disabilities: a five-year review
Pieriboni Giuditta, Buzzi Marina, Leporini Barbara
Education is a universal right that must include anyone. However, students with disabilities often experience significant challenges, obstacles, and frustration when interacting with digital tools and systems, particularly when performing STEM (Science, Technology, Engineering, Mathematics) education paths. This review investigates the current state of ICT-enhanced tools designed to support inclusive STEM education, focusing on the use of applications, systems, games, robots, and platforms aimed at supporting students with disabilities. While the literature reveals growing interest and numerous theoretical frameworks, there remains a lack of concrete, widely adopted applications and systems. Our results highlight the potential of Artificial Intelligence (AI), especially generative AI, in enabling personalized, accessible, and engaging learning experiences conform to the Universal Design for Learning (UDL) principles. However, the full potential of AI in inclusive education is still in its infancy and needs to be further discovered. Co-design with educators, stakeholders, and individuals with disabilities would be essential to accelerate this progress and ensure that future education technologies and systems are truly tailored and suitable for all students, regardless of their abilities and preferences. Digital tools must meet the needs of diverse populations with different disabilities to guarantee equal access to education, while engaging, motivating, and empowering students in science and technology.Source: UNIVERSAL ACCESS IN THE INFORMATION SOCIETY, vol. 25 (issue 1)
DOI: 10.1007/s10209-025-01282-8
Metrics:
Pieriboni Giuditta, Buzzi Marina, Leporini Barbara
Education is a universal right that must include anyone. However, students with disabilities often experience significant challenges, obstacles, and frustration when interacting with digital tools and systems, particularly when performing STEM (Science, Technology, Engineering, Mathematics) education paths. This review investigates the current state of ICT-enhanced tools designed to support inclusive STEM education, focusing on the use of applications, systems, games, robots, and platforms aimed at supporting students with disabilities. While the literature reveals growing interest and numerous theoretical frameworks, there remains a lack of concrete, widely adopted applications and systems. Our results highlight the potential of Artificial Intelligence (AI), especially generative AI, in enabling personalized, accessible, and engaging learning experiences conform to the Universal Design for Learning (UDL) principles. However, the full potential of AI in inclusive education is still in its infancy and needs to be further discovered. Co-design with educators, stakeholders, and individuals with disabilities would be essential to accelerate this progress and ensure that future education technologies and systems are truly tailored and suitable for all students, regardless of their abilities and preferences. Digital tools must meet the needs of diverse populations with different disabilities to guarantee equal access to education, while engaging, motivating, and empowering students in science and technology.Source: UNIVERSAL ACCESS IN THE INFORMATION SOCIETY, vol. 25 (issue 1)
DOI: 10.1007/s10209-025-01282-8
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS
2026
Journal article
Open Access
Robustness of complexity estimation in event-driven signals against accuracy of event detection method
Cafiso Marco, Paradisi Paolo
Complexity has gained recent attention in machine learning for its ability to extract synthetic information from large datasets. Complex dynamical systems are characterized by temporal complexity associated with intermittent birth–death events of self-organizing behavior. These rapid transition events (RTEs) can be modeled as a stochastic point process on the time axis, with inter-event times (IETs) revealing rich dynamics. In particular, IETs with power-law distribution mark a departure from the Poisson statistics and indicate the presence of nontrivial complexity that is quantified by the power-law exponent μ of the IET distribution. However, detection of RTEs in noisy signals remains a challenge, since false positives can obscure the statistical structure of the underlying process. In this paper, we address the problem of quantifying the effect of the event detection tool on the accuracy of complexity estimation. This is reached through a systematic evaluation of the Event-Driven Diffusion Scaling (EDDiS) algorithm, a tool exploiting event-driven diffusion to estimate temporal complexity. After introducing the event detection method RTE-Finder (RTEF), we assess the performance of the RTEF-EDDiS pipeline using event-driven synthetic signals. The reliability of the RTEF is found to strongly depend on parameters such as the percentile and the number of false positives can be much higher than the number of genuine complex events. Despite this, we found that the complexity estimation is quite robust with respect to the rate of false positives. In many of the studied cases, the second moment scaling H appears to even improve as the rate of false positives increases, reaching estimation errors of about 4−7%.Source: CHAOS, SOLITONS AND FRACTALS, vol. 208 (issue 118264)
DOI: 10.1016/j.chaos.2026.118264
DOI: 10.2139/ssrn.5342885
DOI: 10.48550/arxiv.2506.06168
Project(s): Future Artificial Intelligence Research - Spoke 1 “Human-centered AI”
Metrics:
Cafiso Marco, Paradisi Paolo
Complexity has gained recent attention in machine learning for its ability to extract synthetic information from large datasets. Complex dynamical systems are characterized by temporal complexity associated with intermittent birth–death events of self-organizing behavior. These rapid transition events (RTEs) can be modeled as a stochastic point process on the time axis, with inter-event times (IETs) revealing rich dynamics. In particular, IETs with power-law distribution mark a departure from the Poisson statistics and indicate the presence of nontrivial complexity that is quantified by the power-law exponent μ of the IET distribution. However, detection of RTEs in noisy signals remains a challenge, since false positives can obscure the statistical structure of the underlying process. In this paper, we address the problem of quantifying the effect of the event detection tool on the accuracy of complexity estimation. This is reached through a systematic evaluation of the Event-Driven Diffusion Scaling (EDDiS) algorithm, a tool exploiting event-driven diffusion to estimate temporal complexity. After introducing the event detection method RTE-Finder (RTEF), we assess the performance of the RTEF-EDDiS pipeline using event-driven synthetic signals. The reliability of the RTEF is found to strongly depend on parameters such as the percentile and the number of false positives can be much higher than the number of genuine complex events. Despite this, we found that the complexity estimation is quite robust with respect to the rate of false positives. In many of the studied cases, the second moment scaling H appears to even improve as the rate of false positives increases, reaching estimation errors of about 4−7%.Source: CHAOS, SOLITONS AND FRACTALS, vol. 208 (issue 118264)
DOI: 10.1016/j.chaos.2026.118264
DOI: 10.2139/ssrn.5342885
DOI: 10.48550/arxiv.2506.06168
Project(s): Future Artificial Intelligence Research - Spoke 1 “Human-centered AI”
Metrics:
See at:
arXiv.org e-Print Archive | Chaos Solitons & Fractals | doi.org | doi.org | CNR IRIS | www.sciencedirect.com
2026
Other
Open Access
Tecniche di IA Generativa per l'arricchimento di dataset di immagini
Torbidoni Giacomo, Del Corso Giulio, Papini Oscar, Tortorella Domenico
Negli ultimi anni, i sistemi di Deep Learning hanno assunto un ruolo centrale in numerosi ambiti. L'evoluzione delle architetture neurali ha portato allo sviluppo di modelli sempre più complessi e performanti, i quali tuttavia richiedono dataset di addestramento composti anche da decine di milioni di immagini In molti contesti reali non è possibile raggiungere tali numerosità, sia per i costi elevati in termini di tempo e denaro per l'acquisizione e l'annotazione, sia talvolta per la natura specifica del dominio applicativo. Negli ultimi anni, l'emergere dei modelli generativi ha aperto nuove prospettive nell'ambito della data augmentation, consentendo la produzione di dati sintetici realistici e coerenti. Questa tesi, che costituisce la relazione del tirocinio svolto da novembre 2025 a febbraio 2026 presso l'Istituto di Scienza e Tecnologie dell'Informazione del Consiglio Nazionale delle Ricerche (ISTI-CNR) di Pisa nell'ambito del progetto europeo FAITH, di cui ISTI-CNR è il leader del sottoprogetto pilota sul trasporto pubblico, si concentra sulla progettazione, implementazione e validazione di un processo di data augmentation avanzata basato su modelli di intelligenza artificiale generativa, con il duplice obiettivo di ampliare numericamente un dataset esistente e al contempo sviluppare una metodologia controllata e riproducibile per la generazione di immagini sintetiche realistiche e coerenti da integrare in contesti visivi reali.
Torbidoni Giacomo, Del Corso Giulio, Papini Oscar, Tortorella Domenico
Negli ultimi anni, i sistemi di Deep Learning hanno assunto un ruolo centrale in numerosi ambiti. L'evoluzione delle architetture neurali ha portato allo sviluppo di modelli sempre più complessi e performanti, i quali tuttavia richiedono dataset di addestramento composti anche da decine di milioni di immagini In molti contesti reali non è possibile raggiungere tali numerosità, sia per i costi elevati in termini di tempo e denaro per l'acquisizione e l'annotazione, sia talvolta per la natura specifica del dominio applicativo. Negli ultimi anni, l'emergere dei modelli generativi ha aperto nuove prospettive nell'ambito della data augmentation, consentendo la produzione di dati sintetici realistici e coerenti. Questa tesi, che costituisce la relazione del tirocinio svolto da novembre 2025 a febbraio 2026 presso l'Istituto di Scienza e Tecnologie dell'Informazione del Consiglio Nazionale delle Ricerche (ISTI-CNR) di Pisa nell'ambito del progetto europeo FAITH, di cui ISTI-CNR è il leader del sottoprogetto pilota sul trasporto pubblico, si concentra sulla progettazione, implementazione e validazione di un processo di data augmentation avanzata basato su modelli di intelligenza artificiale generativa, con il duplice obiettivo di ampliare numericamente un dataset esistente e al contempo sviluppare una metodologia controllata e riproducibile per la generazione di immagini sintetiche realistiche e coerenti da integrare in contesti visivi reali.
2026
Contribution to book
Open Access
A survey on Good AI: user-centric AI design in healthcare
Berti Andrea, Giannini Valentina, Mazzetti Simone, Pascali Maria Antonietta, Regge Daniele, Colantonio Sara
The integration of Artificial Intelligence (AI) in healthcare has the potential to revolutionize patient care by enhancing diagnostic processes, treatment protocols, and overall healthcare delivery. However, the adoption of AI-powered tools and services is contingent upon establishing a robust foundation of trust among healthcare professionals. The ProCAncer-I project, informed by the FUTURE-AI framework, is at the forefront of this effort, promoting a user-centric design philosophy that prioritizes the needs and expectations of end-users, primarily clinicians and radiologists. This paper delves into the co-design methodology adopted by an interdisciplinary team, elucidating the collaborative efforts that underpin the customization of the FUTURE-AI principles to align with the clinical requirements of the project’s partners. The introduction sets the stage for a comprehensive discussion on the significance of stakeholder engagement in the design and implementation of trustworthy AI systems within clinical settings.Source: SMART INNOVATION, SYSTEMS AND TECHNOLOGIES, vol. 460, pp. 113-128
DOI: 10.1007/978-981-95-4076-1_10
DOI: 10.5281/zenodo.13918943
DOI: 10.5281/zenodo.13918942
Project(s): An AI Platform integrating imaging data and models, supporting precision care through prostate cancer’s continuum, NAVIGATOR
Metrics:
Berti Andrea, Giannini Valentina, Mazzetti Simone, Pascali Maria Antonietta, Regge Daniele, Colantonio Sara
The integration of Artificial Intelligence (AI) in healthcare has the potential to revolutionize patient care by enhancing diagnostic processes, treatment protocols, and overall healthcare delivery. However, the adoption of AI-powered tools and services is contingent upon establishing a robust foundation of trust among healthcare professionals. The ProCAncer-I project, informed by the FUTURE-AI framework, is at the forefront of this effort, promoting a user-centric design philosophy that prioritizes the needs and expectations of end-users, primarily clinicians and radiologists. This paper delves into the co-design methodology adopted by an interdisciplinary team, elucidating the collaborative efforts that underpin the customization of the FUTURE-AI principles to align with the clinical requirements of the project’s partners. The introduction sets the stage for a comprehensive discussion on the significance of stakeholder engagement in the design and implementation of trustworthy AI systems within clinical settings.Source: SMART INNOVATION, SYSTEMS AND TECHNOLOGIES, vol. 460, pp. 113-128
DOI: 10.1007/978-981-95-4076-1_10
DOI: 10.5281/zenodo.13918943
DOI: 10.5281/zenodo.13918942
Project(s): An AI Platform integrating imaging data and models, supporting precision care through prostate cancer’s continuum, NAVIGATOR
Metrics:
See at:
CNR IRIS | link.springer.com | ZENODO | doi.org | ZENODO | ZENODO | CNR IRIS
2026
Conference article
Open Access
Remind me of something? Zero-Shot learning for trustworthy image comparison in rolling stock
Papini Oscar, Del Corso Giulio, Bulotta Davide, Carboni Andrea, Gravili Silvia, Leone Giuseppe Riccardo, Pascali Maria Antonietta, Moroni Davide, Colantonio Sara
This paper discusses the need for trustworthy AI in urban mobility, focusing on high-stakes security applications such as anomaly detection in public transportation. Because the accuracy required to identify potentially dangerous objects often surpasses the capabilities of current models, there is an unavoidable incidence of false positives. We suggest a "learning to defer" approach as a solution. Our technique uses the deep features and label relative importance of a pre-trained classifier (DenseNet/ImageNET-1k) to create a unique item "fingerprint". We then employ a zero-shot meta-learning approach to calibrate the system, enabling it to distinguish between normal background items and genuine anomalies by assigning a similarity score. This method significantly reduces the false "new object" alarms that would otherwise overwhelm human operators. Our proof-of-concept demonstrates that the system is computationally light and can be easily adapted to specific environments and integrated into existing classification modules.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16170, pp. 323-334. Roma, Italy, 15-19/09/2025
DOI: 10.1007/978-3-032-11381-8_28
Project(s): FAITH
Metrics:
Papini Oscar, Del Corso Giulio, Bulotta Davide, Carboni Andrea, Gravili Silvia, Leone Giuseppe Riccardo, Pascali Maria Antonietta, Moroni Davide, Colantonio Sara
This paper discusses the need for trustworthy AI in urban mobility, focusing on high-stakes security applications such as anomaly detection in public transportation. Because the accuracy required to identify potentially dangerous objects often surpasses the capabilities of current models, there is an unavoidable incidence of false positives. We suggest a "learning to defer" approach as a solution. Our technique uses the deep features and label relative importance of a pre-trained classifier (DenseNet/ImageNET-1k) to create a unique item "fingerprint". We then employ a zero-shot meta-learning approach to calibrate the system, enabling it to distinguish between normal background items and genuine anomalies by assigning a similarity score. This method significantly reduces the false "new object" alarms that would otherwise overwhelm human operators. Our proof-of-concept demonstrates that the system is computationally light and can be easily adapted to specific environments and integrated into existing classification modules.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16170, pp. 323-334. Roma, Italy, 15-19/09/2025
DOI: 10.1007/978-3-032-11381-8_28
Project(s): FAITH
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS
2026
Journal article
Restricted
Physiologically inspired modeling of cortical dynamics through spiking neural networks
Milea Dario, Catrambone Vincenzo, Sebastiani Laura, Valenza Gaetano
Objective. The characterization of neural activity underlying neurophysiological function presents a major challenge in computational neuroscience. Several methods have been proposed to investigate cortical network dynamics by reconstructing underlying neural activity from electroencephalography (EEG) signals. However, these methods generally pose significant mathematical challenges.Approach. This study introduces a novel framework to model the underlying brain activity network from a functional and physiologically-inspired perspective, combining spiking neural networks with EEG signal analysis. The dynamics of single neurons are described by the well-known Izhikevich model, and distinct populations of cortical inhibitory and excitatory neurons are employed to model experimental EEG recordings. Functional interactions among distinct populations are mathematically formalized through connective probabilities.Main results. The proposed framework is validated by testing it on synthetic data, as well as on two experimental datasets comprising data from 30 healthy subjects undergoing a cold-pressure test (CPT), and 36 subjects undergoing a mental arithmetic stressor. Experimental results suggest that the proposed framework provides novel and complementary insights into characterizing neuronal changes in comparison to standard EEG power analysis.Significance. The proposed framework constitutes a promising tool for functionally characterizing the underlying cortical dynamics under pathophysiological conditions.Source: JOURNAL OF NEURAL ENGINEERING, vol. 23 (issue 2)
DOI: 10.1088/1741-2552/ae5b27
Metrics:
Milea Dario, Catrambone Vincenzo, Sebastiani Laura, Valenza Gaetano
Objective. The characterization of neural activity underlying neurophysiological function presents a major challenge in computational neuroscience. Several methods have been proposed to investigate cortical network dynamics by reconstructing underlying neural activity from electroencephalography (EEG) signals. However, these methods generally pose significant mathematical challenges.Approach. This study introduces a novel framework to model the underlying brain activity network from a functional and physiologically-inspired perspective, combining spiking neural networks with EEG signal analysis. The dynamics of single neurons are described by the well-known Izhikevich model, and distinct populations of cortical inhibitory and excitatory neurons are employed to model experimental EEG recordings. Functional interactions among distinct populations are mathematically formalized through connective probabilities.Main results. The proposed framework is validated by testing it on synthetic data, as well as on two experimental datasets comprising data from 30 healthy subjects undergoing a cold-pressure test (CPT), and 36 subjects undergoing a mental arithmetic stressor. Experimental results suggest that the proposed framework provides novel and complementary insights into characterizing neuronal changes in comparison to standard EEG power analysis.Significance. The proposed framework constitutes a promising tool for functionally characterizing the underlying cortical dynamics under pathophysiological conditions.Source: JOURNAL OF NEURAL ENGINEERING, vol. 23 (issue 2)
DOI: 10.1088/1741-2552/ae5b27
Metrics:
See at:
CNR IRIS | iopscience.iop.org | CNR IRIS | CNR IRIS
2026
Conference article
Restricted
Reliable and trustworthy learning prototype: insight from POCUS
Ignesti G., D’angelo G., Pratali L., Moroni D., Martinelli M.
Deep learning models often lack the interpretability and trustworthiness required for clinical use. This paper proposes a prototype-regularised training method to analyse 1,208 lung ultrasound images, focusing on B-line artefacts. A ConvNeXt- Tiny architecture is used, adding a novel reconstruction loss to the standard classification loss. The model is guided to extract meaningful prototypes and uses them to classify the ultrasound images. To prevent these constraints from hindering generalisation, it is used in pairs with the proposed reconstruction loss, a set of plausible data augmentation of the ideal researched prototypes, and a geometry-aware network, a spatial transformer network, to measure which solutions help the network towards outputting the most reliable outcomes. The resulting models are precise, lightweight and interpretable, indicating that the proposed solution can be embedded in an ultrasound device to assist healthcare specialists in point-of-care applications.Project(s): TiAssisto
Ignesti G., D’angelo G., Pratali L., Moroni D., Martinelli M.
Deep learning models often lack the interpretability and trustworthiness required for clinical use. This paper proposes a prototype-regularised training method to analyse 1,208 lung ultrasound images, focusing on B-line artefacts. A ConvNeXt- Tiny architecture is used, adding a novel reconstruction loss to the standard classification loss. The model is guided to extract meaningful prototypes and uses them to classify the ultrasound images. To prevent these constraints from hindering generalisation, it is used in pairs with the proposed reconstruction loss, a set of plausible data augmentation of the ideal researched prototypes, and a geometry-aware network, a spatial transformer network, to measure which solutions help the network towards outputting the most reliable outcomes. The resulting models are precise, lightweight and interpretable, indicating that the proposed solution can be embedded in an ultrasound device to assist healthcare specialists in point-of-care applications.Project(s): TiAssisto
2026
Journal article
Open Access
Oxy-inflammatory profile of finishers and non-finishers in an extreme ultra-endurance trail race: the 866-km Transpyrénéa
Mrakic-Sposta Simona, Gussoni Maristella, Mrakic-Sposta Federica, Giardini Guido, Pratali Lorenza, Montorsi Michela, Tonacci Alessandro, Dellanoce Cinzia, Martinelli Massimo, Vezzoli Alessandra
This study investigates the bio-physiological responses occurring under extreme stress conditions and the characterization of the oxy-inflammatory profile of Finishers (FRs) and NoFinishers (NFRs) athletes during the time course and following the Transpyrénéa, an 866 km extreme ultra-race across the French Pyrenees with an altitude difference of 52,900+ m ascent. Thirty-nine experienced ultra-marathon runners (age 43.5 ± 9.1 years; weight 72.1 ± 11.1 kg; BMI 23.3 ± 2.6 kg/m2) were studied using minimally invasive methods on capillary blood and urine samples obtained at baseline (T0), during (T1, 2, 3) and at the end (T4) of the race. Reactive Oxygen Species (ROS) production, total antioxidant capacity (TAC), oxidative damage (8-hydroxy-2-deoxy Guanosine: 8-OH-dG and 8-isoprostane: 8-isoPGF2α), inflammatory (IL-6), nitric oxide pathway (NOx and 3-NT), neopterin, and hematologic (lactate, and hematocrit) biomarkers were assessed. In both FR and NFR athletes a marked systemic increase in ROS, oxidative and nitrosative damage, inflammation, transient immune-renal dysfunction and lactate release were detected throughout the race. Compared to FRs, NFRs displayed significant differences concerning ROS production at T0, 8-isoPGF2-α at T0, T1 and T2, and perceived exertion (RPE score) at T2. These data potentially reflect enhanced adaptative responses to training and metabolic efficacy in FRs, allowing them to better tolerate extreme physiological stress.Source: INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, vol. 27 (issue 10)
DOI: 10.3390/ijms27104295
Metrics:
Mrakic-Sposta Simona, Gussoni Maristella, Mrakic-Sposta Federica, Giardini Guido, Pratali Lorenza, Montorsi Michela, Tonacci Alessandro, Dellanoce Cinzia, Martinelli Massimo, Vezzoli Alessandra
This study investigates the bio-physiological responses occurring under extreme stress conditions and the characterization of the oxy-inflammatory profile of Finishers (FRs) and NoFinishers (NFRs) athletes during the time course and following the Transpyrénéa, an 866 km extreme ultra-race across the French Pyrenees with an altitude difference of 52,900+ m ascent. Thirty-nine experienced ultra-marathon runners (age 43.5 ± 9.1 years; weight 72.1 ± 11.1 kg; BMI 23.3 ± 2.6 kg/m2) were studied using minimally invasive methods on capillary blood and urine samples obtained at baseline (T0), during (T1, 2, 3) and at the end (T4) of the race. Reactive Oxygen Species (ROS) production, total antioxidant capacity (TAC), oxidative damage (8-hydroxy-2-deoxy Guanosine: 8-OH-dG and 8-isoprostane: 8-isoPGF2α), inflammatory (IL-6), nitric oxide pathway (NOx and 3-NT), neopterin, and hematologic (lactate, and hematocrit) biomarkers were assessed. In both FR and NFR athletes a marked systemic increase in ROS, oxidative and nitrosative damage, inflammation, transient immune-renal dysfunction and lactate release were detected throughout the race. Compared to FRs, NFRs displayed significant differences concerning ROS production at T0, 8-isoPGF2-α at T0, T1 and T2, and perceived exertion (RPE score) at T2. These data potentially reflect enhanced adaptative responses to training and metabolic efficacy in FRs, allowing them to better tolerate extreme physiological stress.Source: INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, vol. 27 (issue 10)
DOI: 10.3390/ijms27104295
Metrics:
See at:
CNR IRIS | www.mdpi.com | CNR IRIS
2026
Contribution to journal
Open Access
Multiomics approach for patient stratification and novel target identification in metastatic clear cell renal carcinoma (MeetUro 31): preliminary analysis of radiomics features—A Meet-URO and AIRC study (NCT05782400)
Procopio Giuseppe, Stellato Marco, Barella Marco, Claps Melanie, Guadalupi Valentina, Rametta Alessandro, Basso Umberto, Buti Sebastiano, Vignani Francesca, Fratino Lucia, Nole Franco, Di Napoli Marilena, Zucali Paolo Andrea, Verzonie Lena, Germanese Danila, Di Maio Massimo, Del Re Marzia, De Cecco Loris, Colantonio Sara, Romei Chiara
Artificial Intelligence can integrate clinic-pathological features, radiomics, genomic and transcriptomic analysis to define an optimal allocation strategy in first line treatment of metastatic renal cell carcinoma (mRCC). Methods: This is a multicenter Italian prospective translational study including patients (pts) with clear cell mRCC receiving first-line treatment as per investigator’s choice. Tumor tissue was collected at baseline, plasma samples and CT scan were collected at baseline and every 3 months until progression. Due to the short follow up, here we report the preliminary analysis of the radiomic features to identify signatures associated with Objective Response Rate (ORR). A subset of non-analytically correlated radiomic features was extracted from the selected regions of interest. This subset included first-order statistics, three-dimensional shape descriptors, and texture-based features. All features were computed on the original images using PyRadiomics v.3.1.0. The radiomic analysis pipeline consisted of feature variance filtering, multicollinearity reduction, data harmonization and standardization, and feature importance estimation through a Random Forest-based algorithm. Results: 100 pts were enrolled. For the radiomic analysis, 68 patients were included to ensure a more reliable data harmonization process and to improve the robustness of subsequent analyses. 18 (26%) received IO-IO, 38 (56%) received IO-TKI, 12 (18) received TKI monotherapy as first line treatment. According to IMDC score, 16(24%) were good risk, 39(57%) intermediate and 13(19%) poor. The most common site of metastasis were lung (55%, 38), bone (23%, 16), nodes (20%, 14/68) and liver (13%, 9). In the overall population, ORR was 48% (33), 44% (18) in the IO-TKI group, 44% (8) in the IO-IO group and 42% (5) in the TKI group. The two most influential features identified by the Random Forest model were original_firstorder_Mean and original_glcm_Contrast (0.59 accuracy, 0.58 precision, 0.58 recall, 0.58 F1 score, 0.49 AUROC). Higher values of these features—reflecting increased tissue density and heterogeneity—were associated with a higher ORR. Conclusions: This preliminary analysis suggests that 2 radiomic signatures are associated with higher ORR and are promising as early biomarkers of response in mRCC. However, they do not appear to provide optimal predictive value when used alone, and should therefore be integrated with clinical, genomic, and transcriptomic data to refine predictive modeling. Acknowledgments: We thank AIRC (Associazione Italiana Ricerca sul Cancro) for the support received to conduct this trial. Clinical trial information: NCT05782400.Source: JOURNAL OF CLINICAL ONCOLOGY, vol. 44 (issue 7_suppl), p. 425
DOI: 10.1200/jco.2026.44.7_suppl.425
Metrics:
Procopio Giuseppe, Stellato Marco, Barella Marco, Claps Melanie, Guadalupi Valentina, Rametta Alessandro, Basso Umberto, Buti Sebastiano, Vignani Francesca, Fratino Lucia, Nole Franco, Di Napoli Marilena, Zucali Paolo Andrea, Verzonie Lena, Germanese Danila, Di Maio Massimo, Del Re Marzia, De Cecco Loris, Colantonio Sara, Romei Chiara
Artificial Intelligence can integrate clinic-pathological features, radiomics, genomic and transcriptomic analysis to define an optimal allocation strategy in first line treatment of metastatic renal cell carcinoma (mRCC). Methods: This is a multicenter Italian prospective translational study including patients (pts) with clear cell mRCC receiving first-line treatment as per investigator’s choice. Tumor tissue was collected at baseline, plasma samples and CT scan were collected at baseline and every 3 months until progression. Due to the short follow up, here we report the preliminary analysis of the radiomic features to identify signatures associated with Objective Response Rate (ORR). A subset of non-analytically correlated radiomic features was extracted from the selected regions of interest. This subset included first-order statistics, three-dimensional shape descriptors, and texture-based features. All features were computed on the original images using PyRadiomics v.3.1.0. The radiomic analysis pipeline consisted of feature variance filtering, multicollinearity reduction, data harmonization and standardization, and feature importance estimation through a Random Forest-based algorithm. Results: 100 pts were enrolled. For the radiomic analysis, 68 patients were included to ensure a more reliable data harmonization process and to improve the robustness of subsequent analyses. 18 (26%) received IO-IO, 38 (56%) received IO-TKI, 12 (18) received TKI monotherapy as first line treatment. According to IMDC score, 16(24%) were good risk, 39(57%) intermediate and 13(19%) poor. The most common site of metastasis were lung (55%, 38), bone (23%, 16), nodes (20%, 14/68) and liver (13%, 9). In the overall population, ORR was 48% (33), 44% (18) in the IO-TKI group, 44% (8) in the IO-IO group and 42% (5) in the TKI group. The two most influential features identified by the Random Forest model were original_firstorder_Mean and original_glcm_Contrast (0.59 accuracy, 0.58 precision, 0.58 recall, 0.58 F1 score, 0.49 AUROC). Higher values of these features—reflecting increased tissue density and heterogeneity—were associated with a higher ORR. Conclusions: This preliminary analysis suggests that 2 radiomic signatures are associated with higher ORR and are promising as early biomarkers of response in mRCC. However, they do not appear to provide optimal predictive value when used alone, and should therefore be integrated with clinical, genomic, and transcriptomic data to refine predictive modeling. Acknowledgments: We thank AIRC (Associazione Italiana Ricerca sul Cancro) for the support received to conduct this trial. Clinical trial information: NCT05782400.Source: JOURNAL OF CLINICAL ONCOLOGY, vol. 44 (issue 7_suppl), p. 425
DOI: 10.1200/jco.2026.44.7_suppl.425
Metrics:
See at:
CNR IRIS | www.scilove.app | Journal of Clinical Oncology | CNR IRIS
2026
Other
Embargo
GUARDIANS: Green, Utility, Accessibility, Resilience, Digital Integration, Ability, Nature, Sustainability. Deliverable D1.4: Rapporto analisi AHP sui coefficienti di ponderazione SRI
Emanuele Salerno
Il presente documento riporta i risultati dell'analisi secondo la procedura Analysis of the Hierarchical Process (AHP) condotta con l'aiuto dei partner di progetto e stakeholder esterni sui coefficienti di ponderazione più adeguati per i criteri SRI che il gruppo di supporto tecnico ha giudicato non basabili su dati scientifici oggettivi e ha quindi concluso che dovessero essere pesati in ugual misura.Project(s): Green, Utility, Accessibility, Resilience, Digital Integration, Ability, Nature, Sustainability
Emanuele Salerno
Il presente documento riporta i risultati dell'analisi secondo la procedura Analysis of the Hierarchical Process (AHP) condotta con l'aiuto dei partner di progetto e stakeholder esterni sui coefficienti di ponderazione più adeguati per i criteri SRI che il gruppo di supporto tecnico ha giudicato non basabili su dati scientifici oggettivi e ha quindi concluso che dovessero essere pesati in ugual misura.Project(s): Green, Utility, Accessibility, Resilience, Digital Integration, Ability, Nature, Sustainability
2026
Other
Open Access
Progettazione di un dataset multi-modale di oggetti abbandonati per applicazioni in domini con scarsità di dati
Disperati Alessio, Del Corso Giulio, Leone Giuseppe Riccardo, Papini Oscar, Ceni Andrea
Le moderne soluzioni per la classificazione e l'identificazione di oggetti e persone tramite telecamere si appoggiano su reti neurali addestrate su milioni di immagini naturali. Sebbene efficienti per l'uso quotidiano, le performance di questi metodi degradano in domini applicativi specifici con oggetti poco o per niente rappresentati nei dataset di addestramento. Questa tesi, che costituisce la relazione del tirocinio svolto da ottobre 2025 a gennaio 2026 presso l'Istituto di Scienza e Tecnologie dell'Informazione del Consiglio Nazionale delle Ricerche (ISTI-CNR) di Pisa nell'ambito del progetto europeo FAITH, di cui ISTI-CNR è il leader del sottoprogetto pilota sul trasporto pubblico, si pone l'obiettivo di sopperire a tale mancanza di informazioni mediante la creazione di un dataset multimodale personalizzato nell'ambito del trasporto pubblico, chiamato CTOD (Common Train Objects Dataset). Nel dettaglio, in essa sono illustrate tutte le attività svolte e le scelte progettuali, a partire da quella della telecamera utilizzata per acquisire i dati e la sua validazione, fino ad arrivare alla presentazione del dataset, descrivendone dettagliatamente la definizione, l'organizzazione strutturale e l'implementazione, compreso il modulo associato ad esso per permetterne l'interazione con l'utilizzatore.
Disperati Alessio, Del Corso Giulio, Leone Giuseppe Riccardo, Papini Oscar, Ceni Andrea
Le moderne soluzioni per la classificazione e l'identificazione di oggetti e persone tramite telecamere si appoggiano su reti neurali addestrate su milioni di immagini naturali. Sebbene efficienti per l'uso quotidiano, le performance di questi metodi degradano in domini applicativi specifici con oggetti poco o per niente rappresentati nei dataset di addestramento. Questa tesi, che costituisce la relazione del tirocinio svolto da ottobre 2025 a gennaio 2026 presso l'Istituto di Scienza e Tecnologie dell'Informazione del Consiglio Nazionale delle Ricerche (ISTI-CNR) di Pisa nell'ambito del progetto europeo FAITH, di cui ISTI-CNR è il leader del sottoprogetto pilota sul trasporto pubblico, si pone l'obiettivo di sopperire a tale mancanza di informazioni mediante la creazione di un dataset multimodale personalizzato nell'ambito del trasporto pubblico, chiamato CTOD (Common Train Objects Dataset). Nel dettaglio, in essa sono illustrate tutte le attività svolte e le scelte progettuali, a partire da quella della telecamera utilizzata per acquisire i dati e la sua validazione, fino ad arrivare alla presentazione del dataset, descrivendone dettagliatamente la definizione, l'organizzazione strutturale e l'implementazione, compreso il modulo associato ad esso per permetterne l'interazione con l'utilizzatore.
2026
Journal article
Metadata Only Access
Integrating Multimodal Learning and Explainable AI for Enhanced and Interpretable Prostate Lesion Classification
Giovannoni Claudio, Metta Carlo, Berti Andrea, Colantonio Sara, Monreale Anna, Pratesi Francesca, Rinzivillo Salvatore
Artificial Intelligence systems could find many important applications in the medical field, holding excellent potential for improving disease diagnosis, treatment identification and selection. These opportunities are often jeopardized by the lack of interpretability of such systems, slowing down AI adoption. To overcome the issue, we first introduce an analytical framework exploiting multimodal deep learning for the classification of prostate lesions using Magnetic Resonance Imaging (MRI) data and clinical information on the patients. Then, we propose a multimodal explainability approach based on visual explanations to interpret the proposed model decision-making process and identify how the different modalities contribute to each specific prediction. Our findings, based on the PI-CAI Grand Challenge dataset, demonstrate the potential of combining multimodal data with eXplainable AI (XAI) to enhance prostate cancer diagnosis, improving model predictive performance, interpretability and understanding in treatment decision-making.Source: MACHINE LEARNING, vol. 115 (issue 4)
DOI: 10.1007/s10994-026-07033-x
Project(s): Critical Action Planning over Extreme-Scale Data, Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization, Future Artificial Intelligence Research, It takes two to tango: a synergistic approach to human-machine decision making, Science and technology for the explanation of AI decision making, SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics, Strengthening the Italian RI for Social Mining and Big Data Analytics
Metrics:
Giovannoni Claudio, Metta Carlo, Berti Andrea, Colantonio Sara, Monreale Anna, Pratesi Francesca, Rinzivillo Salvatore
Artificial Intelligence systems could find many important applications in the medical field, holding excellent potential for improving disease diagnosis, treatment identification and selection. These opportunities are often jeopardized by the lack of interpretability of such systems, slowing down AI adoption. To overcome the issue, we first introduce an analytical framework exploiting multimodal deep learning for the classification of prostate lesions using Magnetic Resonance Imaging (MRI) data and clinical information on the patients. Then, we propose a multimodal explainability approach based on visual explanations to interpret the proposed model decision-making process and identify how the different modalities contribute to each specific prediction. Our findings, based on the PI-CAI Grand Challenge dataset, demonstrate the potential of combining multimodal data with eXplainable AI (XAI) to enhance prostate cancer diagnosis, improving model predictive performance, interpretability and understanding in treatment decision-making.Source: MACHINE LEARNING, vol. 115 (issue 4)
DOI: 10.1007/s10994-026-07033-x
Project(s): Critical Action Planning over Extreme-Scale Data, Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization, Future Artificial Intelligence Research, It takes two to tango: a synergistic approach to human-machine decision making, Science and technology for the explanation of AI decision making, SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics, Strengthening the Italian RI for Social Mining and Big Data Analytics
Metrics:
See at:
CNR IRIS
2026
Journal article
Open Access
Localized angle-based unsupervised outlier detection
Zheng Wei, Huang Lili, Liu Haiqiang, Zhu Fa, Shankar Achyut, Rida Imad, Moroni Davide
The angle-based outlier detection (ABOD) is proposed to tackle the “curse of dimensionality” that exists in distance-related or density-related outlier detectors. However, ABOD may fail on multimodal datasets since it only considers global information. Furthermore, ABOD needs to calculate the angles between difference vectors from an instance to each pair of instances in the dataset except itself. Its time complexity reaches O (n3). In order to address these two issues, this paper proposes localized angle-based outlier detection (LABOD) which first finds the influence set, and then calculates the variance of angles between the difference vector from an instance to the mean of its neighbors in the influence set and the difference vectors from the instance to its neighbors in the influence set. The influence set consists of the nearest neighbor set and the reverse nearest neighbor set. Because the variance is defined by the angles in a local region, the proposed method can overcome the drawbacks of ABOD. The experiments performed on both synthetic and benchmark datasets demonstrate that LABOD is superior to ABOD.Source: EGYPTIAN INFORMATICS JOURNAL, vol. 33 (issue 100850)
DOI: 10.1016/j.eij.2025.100850
Metrics:
Zheng Wei, Huang Lili, Liu Haiqiang, Zhu Fa, Shankar Achyut, Rida Imad, Moroni Davide
The angle-based outlier detection (ABOD) is proposed to tackle the “curse of dimensionality” that exists in distance-related or density-related outlier detectors. However, ABOD may fail on multimodal datasets since it only considers global information. Furthermore, ABOD needs to calculate the angles between difference vectors from an instance to each pair of instances in the dataset except itself. Its time complexity reaches O (n3). In order to address these two issues, this paper proposes localized angle-based outlier detection (LABOD) which first finds the influence set, and then calculates the variance of angles between the difference vector from an instance to the mean of its neighbors in the influence set and the difference vectors from the instance to its neighbors in the influence set. The influence set consists of the nearest neighbor set and the reverse nearest neighbor set. Because the variance is defined by the angles in a local region, the proposed method can overcome the drawbacks of ABOD. The experiments performed on both synthetic and benchmark datasets demonstrate that LABOD is superior to ABOD.Source: EGYPTIAN INFORMATICS JOURNAL, vol. 33 (issue 100850)
DOI: 10.1016/j.eij.2025.100850
Metrics:
See at:
CNR IRIS | www.sciencedirect.com | CNR IRIS
2025
Journal article
Open Access
The role of causality in explainable artificial intelligence
Carloni G., Berti A., Colantonio S.
Causality and eXplainable Artificial Intelligence (XAI) have developed as separate fields in computer science, even though the underlying concepts of causation and explanation share common ancient roots. This is further enforced by the lack of review works jointly covering these two fields. In this paper, we investigate the literature to try to understand how and to what extent causality and XAI are intertwined. More precisely, we seek to uncover what kinds of relationships exist between the two concepts and how one can benefit from them, for instance, in building trust in AI systems. As a result, three main perspectives are identified. In the first one, the lack of causality is seen as one of the major limitations of current AI and XAI approaches, and the "optimal" form of explanations is investigated. The second is a pragmatic perspective and considers XAI as a tool to foster scientific exploration for causal inquiry, via the identification of pursue-worthy experimental manipulations. Finally, the third perspective supports the idea that causality is propaedeutic to XAI in three possible manners: exploiting concepts borrowed from causality to support or improve XAI, utilizing counterfactuals for explainability, and considering accessing a causal model as explaining itself. To complement our analysis, we also provide relevant software solutions used to automate causal tasks. We believe our work provides a unified view of the two fields of causality and XAI by highlighting potential domain bridges and uncovering possible limitations.Source: WILEY INTERDISCIPLINARY REVIEWS. DATA MINING AND KNOWLEDGE DISCOVERY
DOI: 10.1002/widm.70015
Project(s): ProCAncer-I
Metrics:
Carloni G., Berti A., Colantonio S.
Causality and eXplainable Artificial Intelligence (XAI) have developed as separate fields in computer science, even though the underlying concepts of causation and explanation share common ancient roots. This is further enforced by the lack of review works jointly covering these two fields. In this paper, we investigate the literature to try to understand how and to what extent causality and XAI are intertwined. More precisely, we seek to uncover what kinds of relationships exist between the two concepts and how one can benefit from them, for instance, in building trust in AI systems. As a result, three main perspectives are identified. In the first one, the lack of causality is seen as one of the major limitations of current AI and XAI approaches, and the "optimal" form of explanations is investigated. The second is a pragmatic perspective and considers XAI as a tool to foster scientific exploration for causal inquiry, via the identification of pursue-worthy experimental manipulations. Finally, the third perspective supports the idea that causality is propaedeutic to XAI in three possible manners: exploiting concepts borrowed from causality to support or improve XAI, utilizing counterfactuals for explainability, and considering accessing a causal model as explaining itself. To complement our analysis, we also provide relevant software solutions used to automate causal tasks. We believe our work provides a unified view of the two fields of causality and XAI by highlighting potential domain bridges and uncovering possible limitations.Source: WILEY INTERDISCIPLINARY REVIEWS. DATA MINING AND KNOWLEDGE DISCOVERY
DOI: 10.1002/widm.70015
Project(s): ProCAncer-I
Metrics:
See at:
CNR IRIS | wires.onlinelibrary.wiley.com | CNR IRIS | CNR IRIS