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InterPARES Trust AI meeting @ Rome

Prof. Emanuele Frontoni attended at Interpares Trust AI global meeting in Rome.

The project aims to support the ongoing availability and accessibility of trustworthy public records by forming a sustainable, ongoing partnership producing original research, training students and other highly qualified personnel, and generating a virtuous circle between academia, archival institutions, government records professionals, and industry.

More info at https://interparestrustai.org/trust

Invited Speaker @Luca Romeo at IJCAI 2021 Workshop WORKSHOP ON AI FOR AGING, REHABILITATION AND INTELLIGENT ASSISTED LIVING (ARIAL)

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We will be online at IJCAI 2021 Workshop WORKSHOP ON AI FOR AGING, REHABILITATION AND INTELLIGENT ASSISTED LIVING (ARIAL) (website), Saturday 21st, 5-6 pm CEST time. We will talk about our recent Kimore Dataset and "The impact of KIMORE dataset for designing clinically meaningful machine learning algorithm for physical rehabilitation Assessment"
Registration here.
Choose W33 - ARIAL from the list
If you are interested to download the dataset please visit our VRAI web page 

Machine Learning based decision support system for early-stage prediction of complications and risk stratification of COVID 19 patients @ IEEE EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH 2021

IEEE EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH 2021
WS: Machine Learning based decision support system for early-stage prediction of complications and risk stratification of COVID 19 patients

Organisers: Luca Romeo, Michele Bernardini, Emanuele Frontoni, Dep. of Information Engineering (DII), Università Politecnica delle Marche, Ancona (Italy); Jonathan Montomoli, Dep. of Intensive Care, Hospital Infermi, Rimini Dep. of Intensive Care Medicine, Erasmus medical Center, Rotterdam, Netherlands; Maggie Cheng, Illinois Institute of Technology, USA; Farshad Firouzi, Duke University, USA

Short Description: During the COVID-19 emergency, intensive care achieved its limit, and the doctors were forced to choose their ICU patients who have the best chance for survival. This worldwide emergency highlighted the need to define a predictive care model capable of providing an accurate estimate of resources and preventive medicine. The analytical capability of machine learning (ML) methods has proven to be extremely accurate and in some cases superior to classical statistical approaches for solving this task. This WS aims to cover all aspects related to ML methodologies for providing risk profiles of the individual patients from which a different intensity of care can be deduced.

SCHEDULE:

Timeline (27 July 2021 CET time 5.00 – 6.15 pm)

5:00 - 5:05 pm: Welcome by the workshop organizers

5:05 - 5:15 pm: Professor Noam Shomron, Predicting Covid19 and other infections, Prof. Noam Shomron, Functional Genomic Team at the Faculty of Medicine at Tel Aviv University

5:15 – 5:25 pm: Professor Elena Giovanna Bignami, Artificial Intelligence in Anesthesia and Perioperative Medicine

5:25 – 5:35 pm: Professor Massih Reza Amini, A Semi-Supervised Multi-Task Learning Approach for Predicting Short-Term Kidney Disease Evolution

5:35 – 5:45 pm: Dr Patrick Thoral, Artificial Intelligence on the Intensive Care unit

5:45 – 5:55 pm: Prof. Khan Shehroz, Anomaly Detection Approach to Identify Early Cases in a Pandemic using Chest X-rays

5:55 – 6:10 pm: Q&A session

6:10 – 6:15 pm: Closing remarks

Workshop website: https://www.bhi-bsn-2021.org/?page_id=3346

Workshop registration: https://www.bhi-bsn-2021.org/?page_id=1169

Unlocking the potential of Artificial Intelligence for UltraSound image processing @ IEEE EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH 2021

IEEE EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH 2021

WS: AI4US: Unlocking the potential of Artificial Intelligence for UltraSound image processing

Organisers: Sara Moccia, PhD - The BioRobotics Institute, Scuola Superiore Sant’Anna and Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy – sara.moccia@santannapisa.it

Prof. Emilio Filippucci, MD, PhD - Rheumatology Unit, Department of Clinical and Molecular Sciences, “Carlo Urbani” Hospital, Jesi, Italy – e.filippucci@univpm.it

Maria Chiara Fiorentino – Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy – m.c.fiorentino@pm.univpm.it

Prof. Emanuele Frontoni – Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy – e.frontoni@staff.univpm.it

Short Description: AI is undertaking a major role in US image analysis. In fact, by tackling the challenges of US image analysis, AI is a valuable tool to further assist clinicians in complex tasks, including lesion identification and automatic biometric  measurements. 

AI4US goal is to group expert AI researchers in US-image analysis to discuss the most recent research work and highlight current challenges and needs. AI4US aims at bridging the gap among universities, hospitals, enterprises and stakeholders to draw a roadmap for future AI applications in the field.

 

SCHEDULE

15:40 - 15:45 pm: Welcome by the workshop organizers 

15:45 - 16:00 pm: Prof. Emilio Filippucci: “The value of artificial intelligence in ultrasound in rheumatology: the clinical perspective”

16:00 - 16:15 pm: Prof. Bruno Madore: “Ultrasound-based sensors to monitor internal motion”

16.15 - 16.30 pm: Nicola Guraschi: “AI in US: a new paradigm to support clinicians in the ultrasound examination”

16.30 - 16.40 pm: Q&A session

16.40 - 16.55 pm: spotlight session and closing remarks

 

Workshop website: https://www.bhi-bsn-2021.org/?page_id=3346

Workshop registration: https://www.bhi-bsn-2021.org/?page_id=1169

"Athanasiou ABME Award" a Sara Moccia

La dott.ssa Sara Moccia è stata premiata con l'Athanasiou ABME Award  per il lavoro “A Review on Advances in Intra-operative Imaging for Surgery and Therapy: Imagining the Operating Room of the Future” (https://link.springer.com/article/10.1007/s10439-020-02553-6). 

Il lavoro, nato dalla collaborazione dell’Università Politecnica delle Marche, il Politecnico di Milano (Prof. Elena De Momi) e l'Università della Magna Graecia di Catanzaro (Prof. Maria Francesca Spadea e Dr. Paolo Zaffino), analizza le nuove generazioni di sala operatoria ibrida, con particolare attenzione alle tecniche di imaging e di analisi di immagini con metodiche di intelligenza artificiale.

Il premio Athanasiou ABME Award è conferito su base annuale dalla rivista Annals of Biomedical Engineering per promuovere la ricerca nel campo dell’Ingegneria Biomedica.

D2CH Tutorial @CVPR 2021

We are glad to be part of CVPR 2021 with the tutorial Deep Digital Cultural Heritage (D2CH) that will be held on June 20, 2021

Program 

Opening Remarks - Marina Paolanti, Università Politecnica delle Marche

Keynote Speaker 1 - Eric li Yi, Stanford/Tsinghua University

Keynote Speaker 2 - Manzil Zaheer, Google

Digital Cultural Heritage: motivations, challenges and opportunities - Roberto Pierdicca, Università Politecnica delle Marche; Francesca Matrone, Politecnico di Torino 

Deep learning framework and code implementation - Yue Wang, Massachusetts Institute of Technology

Semantic segmentation and Explainable AI for Cultural Heritage point clouds - Francesca Matrone, Politecnico di Torino; Davide Manco, Università Politecnica delle Marche 

Organizers 

Marina Paolanti - Università Politecnica delle Marche

Roberto Pierdicca - Università Politecnica delle Marche

Yue Wang - Massachusetts Institute of Technology 

Francesca Matrone - Politecnico di Torino 

Davide Manco - Università Politecnica delle Marche

Andrea Maria Lingua - Politecnico di Torino 

Emanuele Frontoni - Università Politecnica delle Marche

More information is available on the tutorial website (https://d2ch.dii.univpm.it).

Commemorazione Dagmawi @ Ancona

Domenica 13 Giugno 2021 presso la Cattedrale di San Ciriaco il VRAI ha partecipato alla messa di commemorazione del Dr. Dagmawi Mekuria, PhD, che ci ha lasciato circa un anno fa pochi giorni dopo la difesa del suo dottorato.

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A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities

We are happy to share with you our latest work entitled  "A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities". You can find the work here: https://doi.org/10.3390/s21082756

This work stems from the work carried out by Prof. Adriano Mancini and Alessandro Galdelli (Postdoc), in collaboration with “Istituto per le Risorse Biologiche e le Biotecnologie Marine” of the CNR (CNR-IRBIM) on the identification of illegal fishing activities. The authors would thank Prof. Ennio Gambi and Adelmo De Sanctis of Università Politecnica delle Marche for making available the AIS data collected by the Comar receiver installed on the roof of the University.

The paper integrates non-cooperative Synthetic Aperture Radar (SAR) Sentinel-1 images and cooperative Automatic Identification System (AIS) data, by proposing two types of associations: (i) point-to-point and (ii) point-to-line. They allow the fusion of ship positions and highlight “suspicious” AIS data gaps in close proximity of managed areas that can be further investigated only once the vessel—and the gear it adopts—is known. This is addressed by a machine-learning approach based on the Fast Fourier Transform that classifies single sea trips. The approach is tested on a case study in the central Adriatic Sea, automatically reporting AIS-SAR associations and seeking ships that are not broadcasting their positions (intentionally or not).

#vrai #cnr #irbim #ais #sar #fishingeffort #machinelearning #iuufishing #mdpi #sensors

Call for paper - Designing Machine Learning approaches for early-stage prediction of complications and risk stratification of COVID-19 patients

 

The Journal of Medical & Biological Engineering & Computing is now accepting submissions to an upcoming special issue, entitled:

Designing Machine Learning approaches for early-stage prediction of complications and risk stratification of COVID-19 patients

The Guest Editors board is represented also by Luca Romeo, Michele Bernardini, and Emanuele Frontoni of the VRAI Lab.

For further info and deadlines:

Website
https://www.springer.com/journal/11517/updates/19039740

Submit Now
https://www.editorialmanager.com/mbec/default.aspx

 

VRAI MSC-THESIS DISSERTATION

Our MSc student, Davide Manco, succesfully presented his thesis work in the framework of the Master's degree programme in Computer Science Engineering at UNIVPM: 

Generative Adversarial Imitation Learning per la predizione di traiettorie umane in ambito Retail - VIDEO: https://www.youtube.com/watch?v=dzTNnjyO4KE

Congratulation to the new Computer Science Engineer of UNIVPM! 

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