Speed, accuracy, operational skill. There's no competition: the computer's executive power is far superior to human ability. Yet no machine is equipped with instinct, empathy, or an ethical sense.
synbrAIn is working to make sure that human beings can relate responsibly to the machine, to improve people's work and lives.About us
For us they are only the starting point: it is the nature of the data that determines the actual method of analysis. In addition, the goal determines the choice of algorithms. We have to improve the quality of the data as much as possible to effectively instruct the predictive capabilities of neural networks. Often misunderstood or underestimated, this is actually one of the most critical phases of an Artificial Intelligence project.Discover
Testing the diagnostic performance of an AI system in detecting COVID-19 pneumonia and typical bacterial pneumonia in patients who underwent a chest X-ray.
Senticlab won the MEDIMAG-IA hackathon with an artificial intelligence system to recognize bone metastases and calculate their metabolic activity.
A survey on the deep learning technologies which, combined with wearable sensors, could help in diagnosing and monitoring PD.
Senticlab is in the Top 5 in the KiTS 2023 competition, having developed an algorithm for image segmentation of the kidneys, to identify cysts and tumors.
A technique to exploit domain shift during training for medical image 3D segmentation, so that the additional data becomes more usable.
This study describes a computerized support system for microcirculation analysis based on a deep learning approach.
Through the means of Reinforcement Learning, this study explores the possibility of modern AIs to learn during the game.
Investigating the impact of various factors such as language, gender, and recording conditions on the effectiveness of different vocal tasks.
Discover a novel algorithm that uses Mask-RCNN to segment teeth in panoramic dental X-rays with high accuracy and Dice score.
How do you strengthen the team of an artificial intelligence startup? Here is the video of the team building event of synbrAIn and Senticlab of June 2023.
This paper deals with the automatic detection of Myotonia from a task based on the sudden opening of the hand.
A model based on the temporal evolution of speech attractors in the reconstructed phase space to identify hallmarks of PD identification and progression.
This review aims at identifying the most widely employed and promising machine learning methodologies to analyse Parkinson's disease patients voice.
In una video-intervista rilasciata a Story Time, Roberto Pagani (CEO di synbrAIn) racconta l'azienda e le sue attività
In this work, optical, differential air-pressure and acceleration signals, acquired by a chest-worn sensor, are elaborated to feed a deep network.
Following the current state-of-the-art, several ML pipelines were compared, and deep learning was also explored with a custom CNN architecture.
The aim of the study is to show whether it is possible to predict infectious disease outbreaks early, by using machine learning.
This work describes a Clinical Decision Support System for supporting Coeliac Disease diagnosis, based on a neural-network-based fuzzy classifier.
Analysis of the resistance to high levodopa doses of FoG, posture, speech, and altered gait features presenting in daily-ON therapeutic condition.
This work presents the design of a classifier to recognize human emotions from body gestures.
In this work, the influence of obesity and Gastro- Esophageal Reflux Disease (GERD) on voice is assessed, using a machine leargnin approach.
An AI architecture that classifies cell types of human tissue, combining a CNN deep learning model with a wide one.
This study aims at assessing the diagnostic accuracy of minimally invasive point-of-care tests, which may eliminate the need for intestinal biopsy.
This study proposes a method for automatically detecting RBD from single-channel EEG data, by analysing segments recorded during both REM sleep and SWS.
After an intense collaboration that led to the development of MS HUMANAID, Emme Esse invests in synbrAin by subscribing to a reserved capital increase.
This study assesses the feasibility of a telemedicine system for the evaluation of sleep quality through brief vocal recordings.
This work describes an approach to estimate the percentage of COVID-19 specific infection within the lung tissue.
Senticlab has developed a AI-based solution to recognize tumoral nodules from chest radiographs, obtaining great results in the NODE21 competition.
This study analyses the transition regions of specific phonetic groups to model the loss of motor control and the difficulties in movements in PD patients.
The AIDE-X project will support the COVID-19 diagnosis by automatically detect pneumonia from radiographic images.
After several years of collaboration, synbrAIn has acquired SenticLab, with the goal of supporting further growth of the entire company group.
This work presents an algorithm to detect Parkinson's disease based on patients' speech data.
Our partner SenticLab is the runner-up in the MIA-COV19D competition, thanks to a novel AI-based solution for detecting COVID-19 in CT images.
This work presents a comparative analysis of three distinct approaches based on deep learning for COVID-19 detection in chest CTs.
The MS HUMANAID platform by EMME ESSE, with AI modules developed by synbrAIn, starts a fruitful collaboration in the spirit of human-machine cooperation.
The MS HUMANAID platform, which includes the AI modules developed by synbrAIn, will also come into operation in other hospitals in Brianza province, Italy.
This work presents a neural network to identify different histologic sub-regions of gliomas in multi-parametric MRIs and further extracts radiomic features.
Our partner SenticLab developed the best (and winning) solution for identifying tubercolosis within CT scans.
In collaboration with synbrAIn, Emme Esse has released MS HUMANAID, an AI-based software aimed at supporting doctors in COVID-19 pneumonia diagnosis.
Despite the COVID-19 pandemic, Synbrain continues its constant growing in terms of team size, know-how and stability.
This study investigates the role of landmarks labeling before 3D facial acquisition, by comparing several measurements.
An intelligent system that visitors can use to automatically get a description of the scenes shown in a painted wooden ceiling.
The aim of this study is to provide a fluid-dynamic and biological description of unstable and stable (SA) plaques, according to OCT analysis.
This work explains how to exploit a cognitive architecture to model the characters of a story in an interactive storytelling system, accessible a humanoid robot
This work explores how children interact with touchless-enabled interactive displays, comparing their behavior with their chronological age.
This database represents the HNSCC largest available repository and will enable to develop decision support systems and machine learning models.
A novel methodology for 3D reconstruction of coronary artery bifurcations based on the integration of angiography with optical coherence tomography (OCT).
Project AI4NT aims at supporting therapy and diagnosis of patients affected by cognitive diseases, by designing novel artificial intelligence tools.
Projection Pursuit is a methodology for deriving meaningful low-dimensional representations of data.
This work compares several deep learning approaches to automatically detect tuberculosis related lesions in lung CTs.
This study aims at developing a MRI-based radiomic signature as a prognostic marker for different clinical endpoints in NPC patients from non-endemic areas.
This work describes a robotic storytelling system, where the characters have been modelled as cognitive agents embodied in Pepper and NAO robots.
This work describes a methodology for developing an MRI-based radiomic signature for prognosis of overall survival in nasopharyngeal cancer.
This work shows how to build predictor models to forecast users’ interaction duration and distance when interacting via touchless mid-air gestures in public.
The aim of this study is to provide a reliable tool for the automated assessment of postural instability.
This study investigated the added value of end-point specific radiomic signatures (RS) in patients with primary ESTS and RPS.
Data suggest that radiomics can be used to predict outcomes in SCCHN. Here a radiomic model was developed to identify high and low risk of disease recurrence.
This article includes a review of psychological models for inner speech, and a cognitive architecture to implement such capability in robots.
Two multi-metric approaches are proposed for image registration of brain images, combining mutual information and normalized gradient field filter.
The aim of this study is the application of a framework for the in-silico analysis of the disrupted hemodynamics due to an ulcerated lesion.
This work compare the performance of several radiomics‐based predictive models of response to induction chemotherapy (IC) in sinonasal cancers (SNCs).
This work describes an interactive storytelling system, accessible through the SoftBank robotic platforms NAO and Pepper.
This paper describes a 3D reconstruction method based on segmentation of patients' CT images with calcification, metallic artifacts and thrombus removal.
This work investigates classification algorithms, text vectorization and schemes to deal with data imbalance, proposing a novel cost sensitive approach.
This work presents a novel cognitive architecture for inner speech, based on the Standard Model of Mind, integrated with modules for self-talking.
This work uses a virtual phantom to identify a set of radiomic features MRI brain images, which is stable to variations in image acquisition parameters.
This work presents a calculus based on a first-order modal logic, attempting to make the existing inner speech theories suitable for robot.
This works consists in an effective algorithm for creating minimal-size sorting networks, based on incrementally constructing sets of sorting networks.
In this work, the effect of time of repetition (TR) and time of echo (TE) on radiomic features was evaluated using a virtual phantom.
In this work, we report on a usability study carried out on a visual tool for patent infringement detection with 21 professional designers.
This study investigates the difference in terms of usability, effectiveness, and enjoyment perceived by users with ASD between touch and touchless interaction.
This work studies children's interactions with large display via touchless avatar-based interface, investigating the impact of interactiong on learning.
This paper uses Self Organizing Maps, Evolutionary Algorithms and Ant Colony Systems to tackle the MinMax formulation of the Single-Depot Multiple-TSP
This work describes a touchless gesture elicitation and usability study to understand how to perform zoom actions while interacting with desktop displays.
This framework provides a web API to access and process data from coeliac patients, to be used also as a basis for diagnostic decision support systems.
An overview of novel areas of interest in pervasive displays research, based on the works presented during the 7th ACM Intl. Symposium on Pervasive Displays.
In this work, we present and compare two visual approaches to mid-air finger-based menu control in virtual reality environments.
This work presents a new method that uses a bipartite graph for checking the subsumption relation for the optimal-size sorting network problem.
This study validates a segmentation algorithm for the detection of both lumen contours and polymeric bioresorbable scaffold struts from 8-bit OCT images.
Using medical reference texts supported by a specific ontology, we developed Medi-test, a system to generate medical questionnaires in Romanian language.
This work proposes new randomized fitness functions for a genetic algorithm used to solve the satisfiability problem, based on probability amplification.
This work consists in the development of a prognostic model in oral cavity squamous cell carcinoma based using genomics features and MRI radiomics features.
Survival analysis is a method developed for medical research. We exploited it to analyze the time a property stays on market before selling.
This work consists in a framework that helps designers obtain insight on relevant prior art and enables emerging design–prior art comparison.
In this work uses a cognitive architecture for human-robot teaming interaction, to endow a robot with the ability to model its knowledge based on interactions.
This study identifies a set of radiomic features to predict the outcome of induction chemotherapy in sinonasal cancers.
This work presents a reconstruction methodology to compare patient-specific hemodynamics and neo-intimal thickening at nine months from the intervention.
This work introduces the idea of a sound-based system to overcome the display blindness, and some experiments to test its effectiveness.
This study investigates if avatar-based touchless interfaces can foster interest towards artworks, making them more accessible for people affected by ASD.
This work presents a novel technology to create controllable interactive displays, by exploiting thigmonastic behavior of plants such as Mimosa pudica.
This work describes a new way to assess stability and discrimination capacity of radiomic features.
There are a lot of patent analysis systems, with various features. We have designed a visual interface providing an intuitive access to such systems.
This work tackles the MinMax formulation of multiple-TSP, which aims at obtaining balanced subtours for the salesmen, based on an Ant Colony System.
This work proposes a novel approach in Named Entity rEcognition and Linking (NEEL) in tweets, applying similar strategies used for Question Answering (QA).
Touchless systems allow higher accessibility to information provision systems for patients with reduced mobility, thus improving tertiary prevention.
This work describes an information provision system allowing for touchless gestural interactions, along with a trial implementation within a University campus.
This word exploits the correlation between user's affective state and the simultaneous body expressions, to automatically recognize emotions from gestures.
In this study, an OCT-based reconstruction method was developed for the execution of CFD simulations of patient-specific coronary artery models.
KIND‐DAMA is a modular middleware for easing the development of interactive applications based on gestural input, applicable in a plethora of scenarios.
Aim of this study was to assess radiomic features stability and relevance for the analysis of medical images of soft-tissue sarcoma.
This work presents a system able to recognize human body gestures implementing a constrained training set reduction technique, allowing for real-time execution.
This work describes a system for conveying audience emotions during live musical exhibitions, controlling a humanoid robot based on mobile apps.
This work presents a brand new method for centerline extraction of vascular trees, by using computational fluid dynamics (CFD).
This work compares two versions of the same touchless gestural interface, to investigate the effect of user's Avatar on user experience and usability.
This work investigates the influence of a passive audience on the engagement of people with an interactive touchless gestural public display.
This work presents a persuasive pervasive system aimed at influencing users' behavior for reducing energy consumption in buildings.
This study exploits an algorithm based on ant colony optimization for the resolution of Dynamic Vehicle Routing Problem with Time Windows (DVRPTW).
In this study, a fully automatic method was developed for detection of both vessel contours and stent struts in tomography images.
The purpose of this paper is to present a modular middleware for gestural data and devices management.
We believe that the most promising killer application of Ubiquitous Computing is already here: the Human-to-Human Interaction mediated by computers.
In this paper describes a method to detect emotions from gestures using gestural data and a textual description of their meaning.
ChiLab4It is a Question Answering system developer to automatically answer a set Frequently Asked Questions (FAQ).
This paper describes QuASIt, a Question Answering System for the Italian language, and the underlying cognitive architecture.
This paper describes a solution for an innovative and multimodal exploration of ancient book contents, using both touch and touchless gestures.
This paper describes a comparison study aimed at understanding how avatar-based touchless interface affect user experience and usability of interactive displays
An overview about the most used techniques used for gesture recognition, based on data gathered by means of Kinect-like devices.
This paper proposes an innovative approach based on Hidden Markov Models for aligning relational schema and OWL ontologies.
HOWERD is a model for estimating the most likely alignment between an OWL ontology and an Entity Relation Diagram (ERD).
This paper presents an innovative statistical tool to accomplish the alignment between OWL ontologies and Entity Relation Diagrams.
This paper attempts at proposing and evaluating from a bi-criteria perspective several multi-objective Ant Colony Systems to tackle single-depot multiple TSP.
This article describes an objective sampling scheme that can be incorporated in any multi-objective evolutionary algorithm, enhancing its convergence towards the Pareto front.
This work presents two challenges related to touchless networked displays: overcoming interaction blindness and performing evaluations in-the-wild
This work provides an overview of the main challenges in evaluating and designing touchless gestures in-the-wild, using novel user-centred design methods.
In this work, evolutionary algorithms use the information extracted from the previous best solutions in Weighted CSP to guide the search in the next iterations.
This work uses Ant Colony Systems (ACS) for solving the multiple-Traveling Salesman problem (multiple-TSP) with single depot.
The work investigates if evolutionary algorithms could improve their results by means of data mining techniques.
This chapter presents popular meta-heuristics inspired from nature, and in particular focusing on evolutionary computation (EC).
This work aims at identifying the post-treatment time frame for confirming resectability or permanent unresectability in colorectal cancer liver metastases.
This work presents a novel chatbot architecture for the Italian language, implementing cognitive understanding of queries and a suitable disambiguation strategy
This work describes an approach that combines several image processing techniques with the goal of extracting principal palmprint lines.
This work tries to depict the reasons why the actual implementations of IoT paradigm is still far from the original Weiser's vision of ubiquitous computing.
This work describes a novel method to reproduce in real time the opening and closing gestures of a human hand, using data gathered from Kinect-like devices.
This work describes an innovative technique for estimating the most likely composition of ERD constructs that correspond to a given sequence of OWL axioms.
This work analyses the usability of an automatically generated concept map used as a mediator to foster interactions between students and teacher.
This work describes a tool that analyses a document corpus, and generates a semantic space, which in turn can be displayed as a 2D zoomable concept map.
The aim of this study was to analyze the postoperative surgical complications in patients who underwent LAR, LC, or EHO.
This paper describes how to use neural networks to detect in real time hand poses, based on data gathered from a Microsoft Kinect RGB-D sensor.
This work describes a system able to analyse a dataset of plant images, and classify them based on LIRe, metadata clustering and naive Bayes classification.
This work is aimed at evaluating and comparing two different versions of an information provision system deployed in two editions of a large and crowded fair.
We present a novel semantic tokenizer that finds the sub-words through an application of the A* based search algorithm.
This work presents a method to deliver non-linear projections of a data set that discriminate between existing labeled groups of data items.
This work describes a framework for linear feature extraction applicable in both unsupervised, semi-supervised and supervised data analysis.
This work uses Support Vector Regression (SVR) to synthesize missing compressional acoustic or sonic logs when only common logs are available.
This work proposes and investigates a new method to identify outliers in multivariate numerical data, driving its roots in projection pursuit.
This work explores several case studies to show how mobile devices may become part of a memorable experience during a visit to museums or exhibits.
The work presented in this paper deals with an attempt to enrich a database structure using linguistic information.
This work proposes a new method to identify significant structures in data and cluster them, based on the projection pursuit methodology.
This work outlines a novel framework inspired to Cognitive Linguistics theories to allow semantic annotation.
QRouteMe is a multichannel information system built to ensure rich user experiences in exhibits and museums.
This work presents a multichannel information system to build and deliver rich user experiences in exhibits and museums.
This work describes the evolution of HCI, and how it paved the way for novel current and upcoming human-to human interaction ways.
This work combines principles from two different clustering paradigms: the standard k-Means algorithm, hybridized with Particle Swarm Optimization.
Human-to-Human Interaction (HHI) describes how today's human interaction is largely indirect and mediated by a wide variety of technologies and devices.
This work addresses the clustering problem given the similarity matrix of a dataset,using two distinct criteria to minimize cut size and balance clusters.
This work introduces a genetic algorithm enhanced with a trap escaping strategy derived from the dual information presented as discrete Lagrange multipliers.
This work presents a conversational agent intended to act as a part of an educational system.
This work describes a new architectural framework for a metacognitive tutoring system that is aimed to stimulate self-regulatory behavior in the learner.
This work introduces an objective function for unsupervised clustering able to guide the search for significant features and optimal partitions.
This paper presents a hierarchical framework for automatic semantic annotation of plain text, with the goal of converting wiki pages into semantic wikis.
This study is about the application of messy genetic algorithms for the winner determination problem in the combinatorial auction realm.
This work describes a project that addresses two issues related to simplifying and broadening augmented environment access.
The work in unsupervised learning centered on clustering has been extended with new paradigms to address the demands raised by real-world problems.
The work describes an open and modular e-learning software platform to support highly cognitive tasks performed by the main actors of the learning process.
This work describes a modality to access and to organize unstructured contents related to a particular topic coming from the access to Wikipedia pages
This novel evolutionary computing strategy uses linear programming duality information to help the search for optimum solutions of hard optimization problems.
This work describes a method to compare left and right mammographic views of the same patient as in CC and as MLO projection.
This work presents a solution that allows mobile users to remotely access an automatic technique for personal photo album management.
This chapter focuses on the combination of evolutionary computation (EC ) techniques and constraint satisfaction problems (CSPs).
This work integrates unsupervised feature selection with ensemble clustering in order to deliver more accurate data partitions.
This work presents WikiArt, a system able to integrate three different kinds of information sources: a database, a wiki, and an ontology.
This paper presents a methodology to acquire new knowledge in TutorJ using external information sources.
This work investigates feature weighting and selection in the context of unsupervised clustering.
This work presents an evaluation system whose goal is to build a flexible and easy way to manage resources in a personalized manner.
This work uses RFID technology together with a conversational agent in order to implement a multimodal information retrieval service we call sensor mesh.
This work discusses the development of a new set of Visual API to embed a remote application control within an application running on a PDA and vice-versa.
This work is about the role of multimodality in intelligent, mobile guides for cultural heritage environments.
This work describes a method to reduce the FP/imm number through CC and MLO mammographic views comparison of the same patient.
This work describes a method for enhancing the performance of clustering algorithms, exploiting Particle Swarm Optimization techniques.
This work aims at making the E2D-HUM algorithm, used to correct MRI bias artifacts, available as a service on a grid infrastructure.
SmartTraffic is a Java-based communication wrapper to allow programmers to seamlessly use TCP or UDP protocols over Bluetooth or any IP-based wireless network.
This work discusses the design of a PDA-driven remote display control system which was designed for a pervasive computing scenario.
This work proposes a smart, human-like PDA-based personal shopper assistant that is able to understand user needs through a natural language interaction.
This work proposes a system for an "Opportunistic Chat", allowing users to exchange messages over Bluetooth and/or TCP/IP connections.
This work discusses a user-friendly, multi-modal guide system for pervasive context-aware service provision within augmented environments.
This article introduces a new scheme for solving the general max constraint satisfaction problem based on Particle Swarm Optimization.
This work illustrates a pervasive, multimodal virtual guide for a cultural heritage site tour.
This work presents a simple way of combining inference with stochastic search for solving constraint satisfaction problems.
This work presents a general scheme based on a genetic algorithm and the particle swarm optimization heuristic to solve constraint satisfaction problems.
This work discusses a novel technique for user identification and position sensing in augmented reality by the use of RFID tags and cameras.
The Agent Network for Bluetooth Devices is a system that uses personal mobile devices to supply people with ad hoc information and high-level services.
This work describes a multi-modal system for pervasive context-aware service provision, allowing user interaction by means of PDAs or smartphones.
The aim of this work is to present two heuristics vaguely inspired from the evolution of star systems.
This work describes a novel approach to the problem of arranging a Bluetooth based positioning system capable of accurately providing people coordinates.
This work presents an attempt to transform particle swarm optimization (PSO) into a self-adaptive algorithm based on specific swarm-inspired operators.
This work describes a model which can be used to describe hybrid entities in an augmented reality environment.
This work discusses three mobile agent application fields: parallel and distributed computing, data mining and information retrieval, and networking.
This work describes how mobile agents can serve as an effective solution for grid service provision.
This work discusses grid technology and some related problem, providing also an overview of grids, in terms of application fields and required protocols.
This work discusses a mobile agent based tool for arranging communities whose members want to share computing resources.