Carlo Sansone | Università degli Studi di Napoli "Federico II" (original) (raw)

Papers by Carlo Sansone

Information

An intelligent conversational agent for the legal domain is an AI-powered system that can communi... more An intelligent conversational agent for the legal domain is an AI-powered system that can communicate with users in natural language and provide legal advice or assistance. In this paper, we present CREA2, an agent designed to process legal concepts and be able to guide users on legal matters. The conversational agent can help users navigate legal procedures, understand legal jargon, and provide recommendations for legal action. The agent can also give suggestions helpful in drafting legal documents, such as contracts, leases, and notices. Additionally, conversational agents can help reduce the workload of legal professionals by handling routine legal tasks. CREA2, in particular, will guide the user in resolving disputes between people residing within the European Union, proposing solutions in controversies between two or more people who are contending over assets in a divorce, an inheritance, or the division of a company. The conversational agent can later be accessed through vario...

IEEE Journal of Biomedical and Health Informatics

Procedia Engineering, 2011

Information

Bullying and cyberbullying are harmful social phenomena that involve the intentional, repeated us... more Bullying and cyberbullying are harmful social phenomena that involve the intentional, repeated use of power to intimidate or harm others. The ramifications of these actions are felt not just at the individual level but also pervasively throughout society, necessitating immediate attention and practical solutions. The BullyBuster project pioneers a multi-disciplinary approach, integrating artificial intelligence (AI) techniques with psychological models to comprehensively understand and combat these issues. In particular, employing AI in the project allows the automatic identification of potentially harmful content by analyzing linguistic patterns and behaviors in various data sources, including photos and videos. This timely detection enables alerts to relevant authorities or moderators, allowing for rapid interventions and potential harm mitigation. This paper, a culmination of previous research and advancements, details the potential for significantly enhancing cyberbullying detec...

Diagnostics

The Prostate Imaging Reporting and Data System (PI-RADS) classification is based on a scale of va... more The Prostate Imaging Reporting and Data System (PI-RADS) classification is based on a scale of values from 1 to 5. The value is assigned according to the probability that a finding is a malignant tumor (prostate carcinoma) and is calculated by evaluating the signal behavior in morphological, diffusion, and post-contrastographic sequences. A PI-RADS score of 3 is recognized as the equivocal likelihood of clinically significant prostate cancer, making its diagnosis very challenging. While PI-RADS values of 4 and 5 make biopsy necessary, it is very hard to establish whether to perform a biopsy or not in patients with a PI-RADS score 3. In recent years, machine learning algorithms have been proposed for a wide range of applications in medical fields, thanks to their ability to extract hidden information and to learn from a set of data without previous specific programming. In this paper, we evaluate machine learning approaches in detecting prostate cancer in patients with PI-RADS score ...

IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004, 2004

... Using Support Vector Machines Gianluca Antonelli Fabrizio Caccavale Carlo Sansone and Luigi V... more ... Using Support Vector Machines Gianluca Antonelli Fabrizio Caccavale Carlo Sansone and Luigi Villani Universith di Cassino Ka G. Di Biasio 43 03043 Cassino, Italy 85100 Potenza, Italy 80125 Napoli, Italy DAEIMI DIFA DIS ...

2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), 2015

Conference Proceedings. 10th Anniversary. IMTC/94. Advanced Technologies in I & M. 1994 IEEE Instrumentation and Measurement Technolgy Conference (Cat. No.94CH3424-9)

Among the advantages offered by the Artificial Neural Networks (ANNs), in the analysis and active... more Among the advantages offered by the Artificial Neural Networks (ANNs), in the analysis and active control of structures characterized by high modal densities and complexity, it should be mentioned the possibility of optimizing the number and the position of sensors and actuators. This feature can result in a sensible reduction of cost in the analysis and control of large structures

2009 IEEE International Workshop on Medical Measurements and Applications, MeMeA 2009, 2009

Abstract—Biological imaging has recently received a strong impulse by the development of fluoresc... more Abstract—Biological imaging has recently received a strong impulse by the development of fluorescent probes and new high-resolution microscopes. It is presently having a profound impact on the way research is being conducted in the life sciences. Biologists can now visualize ...

Future Internet

Continual mapping and monitoring of impervious surfaces are crucial activities to support sustain... more Continual mapping and monitoring of impervious surfaces are crucial activities to support sustainable urban management strategies and to plan effective actions for environmental changes. In this context, impervious surface coverage is increasingly becoming an essential indicator for assessing urbanization and environmental quality, with several works relying on satellite imagery to determine it. However, although satellite imagery is typically available with a frequency of 3–10 days worldwide, imperviousness maps are released at most annually as they require a huge human effort to be produced and validated. Attempts have been made to extract imperviousness maps from satellite images using machine learning, but (i) the scarcity of reliable and detailed ground truth (ii) together with the need to manage different spectral bands (iii) while making the resulting system easily accessible to the end users is limiting their diffusion. To tackle these problems, in this work we introduce a d...

Image Analysis and Processing - ICIAP 2017, 2017

Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is gaining popularity as a complem... more Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is gaining popularity as a complementary diagnostic method for early detection and diagnosis of breast cancer. However, due to the large amount of data, DCE-MRI can hardly be inspected without the use of a Computer Aided Diagnosis (CAD) system. Among the major issues in developing CAD for breast DCE-MRI there is the classification of regions of interest according to their aggressiveness. For this task newer hand-crafted features are continuously proposed by domain experts. On the other hand, deep learning approaches have gained popularity in many pattern recognition tasks, being able to outperform classical machine learning techniques in different fields, by learning compact hierarchical representations of an image which well fit the specific task to solve. The aim of this work is to explore the applicability of Convolutional Neural Networks (CNN) in automatic lesion malignancy assessment for breast DCE-MRI data. Our findings show that while promising results in treating DCE-MRI can be obtained by using transfer learning, CNNs have to be carefully designed and tuned in order to outperform approaches specifically designed to exploit all the available data information.

IET Computer Vision, 2018

Data collection and analysis are becoming more and more important in a variety of application dom... more Data collection and analysis are becoming more and more important in a variety of application domains as long as the novel technologies advance. At the same time, we are experiencing a growing need for human-machine interaction with expert systems pushing research through new knowledge representation models and interaction paradigms. In particular, in the last years eHealth - that indicates all the health-care practices supported by electronic elaboration and remote communications - calls for the availability of smart environment and big computational resources. The aim of this paper is to introduce the HOLMeS (Health On-Line Medical Suggestions) framework. The introduced system proposes to change the eHealth paradigm where a trained machine learning algorithm, deployed on a cluster-computing environment, provides medical suggestion via both chat-bot and web-app modules. The chat-bot, based on deep learning approaches, is able to overcome the limitation of biased interaction between...

Frontiers in oncology, 2018

Radiomics leverages existing image datasets to provide non-visible data extraction via image post... more Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contra...

2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2015

ABSTRACT This paper presents an analysis on the suitability of a low cost motion capture system t... more ABSTRACT This paper presents an analysis on the suitability of a low cost motion capture system to support the development of generic applications within medical scenarios. In this study we used the Microsoft Kinect (first generation) sensor as basic technology for its wide availability and the relatively low cost, which generated great interest of the scientific community in several application fields. Most of the studies found in literature however, do not address the quality and reliability of the raw data (objects position and depth) considering only the results of the integrated 3D recognition algorithms for the final skeleton/gesture analysis. This can be critical in ehabilitation, in which the knowledge of accuracy, reliability and performance in tracking patient body movements is required and assessed. To this aim, this article proposes an experimental protocol to analyze the Kinect measurement capabilities, via the definition of peculiar parameters. Here we also introduced an analysis of performance versus usage time. Results showed some weaknesses of the sensor. Specifically, resolution was found in the range of tens of millimeters while degrading over time. The adoption of the Microsoft Kinect (first generation) in medical scenarios should therefore be considered with respect to the application it serves having clear in mind its performances and limitations.

Computer Methods and Programs in Biomedicine, 2015

2014 International Conference on Intelligent Networking and Collaborative Systems, 2014

ABSTRACT The last generation automated security and surveillance systems call for new and advance... more ABSTRACT The last generation automated security and surveillance systems call for new and advanced capabilities to automatically and reliably recognize suspicious events or activities in the monitored environments on the base of a real- time and combined analysis of different multimedia streams. In this paper we focus our attention on the analysis of audio signal and present a method based on one-class Support Vector Machine (1-SVM) classifiers. Such an approach is able to support the recognition of different kinds of burst-like anoma- lies (i.e. gun-shots, broken glasses and screams), on the base of their time and frequency domain characterization. Several experiments have been carried out, showing the potentiality of our method with respect to other approaches proposed in the recent literature.

Journal of Imaging, 2021

The recent spread of Deep Learning (DL) in medical imaging is pushing researchers to explore its ... more The recent spread of Deep Learning (DL) in medical imaging is pushing researchers to explore its suitability for lesion segmentation in Dynamic Contrast-Enhanced Magnetic-Resonance Imaging (DCE-MRI), a complementary imaging procedure increasingly used in breast-cancer analysis. Despite some promising proposed solutions, we argue that a “naive” use of DL may have limited effectiveness as the presence of a contrast agent results in the acquisition of multimodal 4D images requiring thorough processing before training a DL model. We thus propose a pipelined approach where each stage is intended to deal with or to leverage a peculiar characteristic of breast DCE-MRI data: the use of a breast-masking pre-processing to remove non-breast tissues; the use of Three-Time-Points (3TP) slices to effectively highlight contrast agent time course; the application of a motion-correction technique to deal with patient involuntary movements; the leverage of a modified U-Net architecture tailored on th...

Image Analysis and Processing - ICIAP 2017, 2017

Information

An intelligent conversational agent for the legal domain is an AI-powered system that can communi... more An intelligent conversational agent for the legal domain is an AI-powered system that can communicate with users in natural language and provide legal advice or assistance. In this paper, we present CREA2, an agent designed to process legal concepts and be able to guide users on legal matters. The conversational agent can help users navigate legal procedures, understand legal jargon, and provide recommendations for legal action. The agent can also give suggestions helpful in drafting legal documents, such as contracts, leases, and notices. Additionally, conversational agents can help reduce the workload of legal professionals by handling routine legal tasks. CREA2, in particular, will guide the user in resolving disputes between people residing within the European Union, proposing solutions in controversies between two or more people who are contending over assets in a divorce, an inheritance, or the division of a company. The conversational agent can later be accessed through vario...

IEEE Journal of Biomedical and Health Informatics

Procedia Engineering, 2011

Information

Bullying and cyberbullying are harmful social phenomena that involve the intentional, repeated us... more Bullying and cyberbullying are harmful social phenomena that involve the intentional, repeated use of power to intimidate or harm others. The ramifications of these actions are felt not just at the individual level but also pervasively throughout society, necessitating immediate attention and practical solutions. The BullyBuster project pioneers a multi-disciplinary approach, integrating artificial intelligence (AI) techniques with psychological models to comprehensively understand and combat these issues. In particular, employing AI in the project allows the automatic identification of potentially harmful content by analyzing linguistic patterns and behaviors in various data sources, including photos and videos. This timely detection enables alerts to relevant authorities or moderators, allowing for rapid interventions and potential harm mitigation. This paper, a culmination of previous research and advancements, details the potential for significantly enhancing cyberbullying detec...

Diagnostics

The Prostate Imaging Reporting and Data System (PI-RADS) classification is based on a scale of va... more The Prostate Imaging Reporting and Data System (PI-RADS) classification is based on a scale of values from 1 to 5. The value is assigned according to the probability that a finding is a malignant tumor (prostate carcinoma) and is calculated by evaluating the signal behavior in morphological, diffusion, and post-contrastographic sequences. A PI-RADS score of 3 is recognized as the equivocal likelihood of clinically significant prostate cancer, making its diagnosis very challenging. While PI-RADS values of 4 and 5 make biopsy necessary, it is very hard to establish whether to perform a biopsy or not in patients with a PI-RADS score 3. In recent years, machine learning algorithms have been proposed for a wide range of applications in medical fields, thanks to their ability to extract hidden information and to learn from a set of data without previous specific programming. In this paper, we evaluate machine learning approaches in detecting prostate cancer in patients with PI-RADS score ...

IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004, 2004

... Using Support Vector Machines Gianluca Antonelli Fabrizio Caccavale Carlo Sansone and Luigi V... more ... Using Support Vector Machines Gianluca Antonelli Fabrizio Caccavale Carlo Sansone and Luigi Villani Universith di Cassino Ka G. Di Biasio 43 03043 Cassino, Italy 85100 Potenza, Italy 80125 Napoli, Italy DAEIMI DIFA DIS ...

2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), 2015

Conference Proceedings. 10th Anniversary. IMTC/94. Advanced Technologies in I & M. 1994 IEEE Instrumentation and Measurement Technolgy Conference (Cat. No.94CH3424-9)

Among the advantages offered by the Artificial Neural Networks (ANNs), in the analysis and active... more Among the advantages offered by the Artificial Neural Networks (ANNs), in the analysis and active control of structures characterized by high modal densities and complexity, it should be mentioned the possibility of optimizing the number and the position of sensors and actuators. This feature can result in a sensible reduction of cost in the analysis and control of large structures

2009 IEEE International Workshop on Medical Measurements and Applications, MeMeA 2009, 2009

Abstract—Biological imaging has recently received a strong impulse by the development of fluoresc... more Abstract—Biological imaging has recently received a strong impulse by the development of fluorescent probes and new high-resolution microscopes. It is presently having a profound impact on the way research is being conducted in the life sciences. Biologists can now visualize ...

Future Internet

Continual mapping and monitoring of impervious surfaces are crucial activities to support sustain... more Continual mapping and monitoring of impervious surfaces are crucial activities to support sustainable urban management strategies and to plan effective actions for environmental changes. In this context, impervious surface coverage is increasingly becoming an essential indicator for assessing urbanization and environmental quality, with several works relying on satellite imagery to determine it. However, although satellite imagery is typically available with a frequency of 3–10 days worldwide, imperviousness maps are released at most annually as they require a huge human effort to be produced and validated. Attempts have been made to extract imperviousness maps from satellite images using machine learning, but (i) the scarcity of reliable and detailed ground truth (ii) together with the need to manage different spectral bands (iii) while making the resulting system easily accessible to the end users is limiting their diffusion. To tackle these problems, in this work we introduce a d...

Image Analysis and Processing - ICIAP 2017, 2017

Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is gaining popularity as a complem... more Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is gaining popularity as a complementary diagnostic method for early detection and diagnosis of breast cancer. However, due to the large amount of data, DCE-MRI can hardly be inspected without the use of a Computer Aided Diagnosis (CAD) system. Among the major issues in developing CAD for breast DCE-MRI there is the classification of regions of interest according to their aggressiveness. For this task newer hand-crafted features are continuously proposed by domain experts. On the other hand, deep learning approaches have gained popularity in many pattern recognition tasks, being able to outperform classical machine learning techniques in different fields, by learning compact hierarchical representations of an image which well fit the specific task to solve. The aim of this work is to explore the applicability of Convolutional Neural Networks (CNN) in automatic lesion malignancy assessment for breast DCE-MRI data. Our findings show that while promising results in treating DCE-MRI can be obtained by using transfer learning, CNNs have to be carefully designed and tuned in order to outperform approaches specifically designed to exploit all the available data information.

IET Computer Vision, 2018

Data collection and analysis are becoming more and more important in a variety of application dom... more Data collection and analysis are becoming more and more important in a variety of application domains as long as the novel technologies advance. At the same time, we are experiencing a growing need for human-machine interaction with expert systems pushing research through new knowledge representation models and interaction paradigms. In particular, in the last years eHealth - that indicates all the health-care practices supported by electronic elaboration and remote communications - calls for the availability of smart environment and big computational resources. The aim of this paper is to introduce the HOLMeS (Health On-Line Medical Suggestions) framework. The introduced system proposes to change the eHealth paradigm where a trained machine learning algorithm, deployed on a cluster-computing environment, provides medical suggestion via both chat-bot and web-app modules. The chat-bot, based on deep learning approaches, is able to overcome the limitation of biased interaction between...

Frontiers in oncology, 2018

Radiomics leverages existing image datasets to provide non-visible data extraction via image post... more Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contra...

2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2015

ABSTRACT This paper presents an analysis on the suitability of a low cost motion capture system t... more ABSTRACT This paper presents an analysis on the suitability of a low cost motion capture system to support the development of generic applications within medical scenarios. In this study we used the Microsoft Kinect (first generation) sensor as basic technology for its wide availability and the relatively low cost, which generated great interest of the scientific community in several application fields. Most of the studies found in literature however, do not address the quality and reliability of the raw data (objects position and depth) considering only the results of the integrated 3D recognition algorithms for the final skeleton/gesture analysis. This can be critical in ehabilitation, in which the knowledge of accuracy, reliability and performance in tracking patient body movements is required and assessed. To this aim, this article proposes an experimental protocol to analyze the Kinect measurement capabilities, via the definition of peculiar parameters. Here we also introduced an analysis of performance versus usage time. Results showed some weaknesses of the sensor. Specifically, resolution was found in the range of tens of millimeters while degrading over time. The adoption of the Microsoft Kinect (first generation) in medical scenarios should therefore be considered with respect to the application it serves having clear in mind its performances and limitations.

Computer Methods and Programs in Biomedicine, 2015

2014 International Conference on Intelligent Networking and Collaborative Systems, 2014

ABSTRACT The last generation automated security and surveillance systems call for new and advance... more ABSTRACT The last generation automated security and surveillance systems call for new and advanced capabilities to automatically and reliably recognize suspicious events or activities in the monitored environments on the base of a real- time and combined analysis of different multimedia streams. In this paper we focus our attention on the analysis of audio signal and present a method based on one-class Support Vector Machine (1-SVM) classifiers. Such an approach is able to support the recognition of different kinds of burst-like anoma- lies (i.e. gun-shots, broken glasses and screams), on the base of their time and frequency domain characterization. Several experiments have been carried out, showing the potentiality of our method with respect to other approaches proposed in the recent literature.

Journal of Imaging, 2021

The recent spread of Deep Learning (DL) in medical imaging is pushing researchers to explore its ... more The recent spread of Deep Learning (DL) in medical imaging is pushing researchers to explore its suitability for lesion segmentation in Dynamic Contrast-Enhanced Magnetic-Resonance Imaging (DCE-MRI), a complementary imaging procedure increasingly used in breast-cancer analysis. Despite some promising proposed solutions, we argue that a “naive” use of DL may have limited effectiveness as the presence of a contrast agent results in the acquisition of multimodal 4D images requiring thorough processing before training a DL model. We thus propose a pipelined approach where each stage is intended to deal with or to leverage a peculiar characteristic of breast DCE-MRI data: the use of a breast-masking pre-processing to remove non-breast tissues; the use of Three-Time-Points (3TP) slices to effectively highlight contrast agent time course; the application of a motion-correction technique to deal with patient involuntary movements; the leverage of a modified U-Net architecture tailored on th...

Image Analysis and Processing - ICIAP 2017, 2017