Program and Accepted Papers

1st AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2022)

The HC@AIxIA 2022 Workshop (and the AIxIA Working Group on Artificial Intelligence for Healthcare)

You can find some figures and stats about the workshop, alongside information about the AIxIA working group for Artificial Intelligence for Healthcare, at the link below.

For joining the working group, please drop us an email here: hc-aixia@googlegroups.com.

Venue

The Workshop took place in the room W1, on the second floor of the library building "Biblioteca Rizzi". Address: Via delle Scienze 206, Udine, Italy. Google Maps: https://goo.gl/maps/7ezSKoEsD83WCEBT6.

Further details on venue and directions are available on the main conference (AIxIA 2022) website: https://aixia2022.uniud.it/workshops.

Instructions for Speakers

  • Each accepted work have been presented live, and have been given a slot of 15 mins (details have been privately sent to ALL authors of accepted papers). For any issues please contact hc-aixia@googlegroups.com.

  • Please remember that for a paper to be presented at least one author must have registered at AIxIA 2022 and planned to attend the HC@AIxIA 2022 Workshop.

AIxIA working group on Artificial Intelligence for Healthcare - KICK-OFF Meeting

All authors of accepted papers have been invited to participate in the Kick-Off meeting of the AI for Healthcare Working Group of AIxIA, that took place on November 30rd, 2022 right after the workshop.

Best Paper Award

A subset of the original works have been selected by the Program Committee (PC) based on the review scores. All PC members and all workshop attendees have been called to participate in a poll for selecting the Best Paper among the shortlisted ones. Chairs have the pleasure to announce that the HC@AIxIA 2022 Best Paper Award has been awarded to the work:

  • Andrea Santomauro, Luigi Portinale and Giorgio Leonardi. A multimodal approach to automated generation of radiology reports using contrastive learning

Workshop Program

November 30th, 2022

Pre-Prints of Accepted Papers

Shared during the Workshop - Access disabled afterwards

Please have a look at the proceedings once available.

List of Accepted Papers (and slides, if authors made them available)

Original Contributions

  • Alessio Zanga, Alice Bernasconi, Peter Lucas, Hanny Pijnenborg, Casper Reijnen, Marco Scutari and Fabio Stella. Risk Assessment of Lymph Node Metastasis in Endometrial Cancer Patients: A Causal Approach - SLIDES

  • Andrea Santomauro, Luigi Portinale and Giorgio Leonardi. A multimodal approach to automated generation of radiology reports using contrastive learning - SLIDES

  • Mario Garbelli, Francesco Bellocchio and Luca Neri. Arteriovenous fistula flow level prediction through ordinal classifier methods - SLIDES

  • Tania Bailoni, Mauro Dragoni and Ivan Donadello. Modeling a Functional Status Knowledge Graph For Personal Health - SLIDES

  • Muhammad Suffian, Sara Montagna, Alessandro Bogliolo, Claudio Ortolani, Stefano Papa and Mario D'Atri. Machine learning for automated gating of flow cytometry data - SLIDES

  • Mihai Horia Popescu, Kevin Roitero and Vincenzo Della Mea. Explainable Classification of Medical Documents Through a Text-to-Text Transformer - SLIDES

  • Alessandro Quarta, Pierangela Bruno and Francesco Calimeri. Continual Learning for medical image classification - SLIDES

  • Simone Scaboro, Beatrice Portelli and Giuseppe Serra. Detection of Adverse Drug Events from Social Media Texts - Research Project Overview - SLIDES

  • Enrico Mensa, Daniele Liberatore, Davide Colla, Matteo Delsanto, Marco Giustini and Daniele P. Radicioni. Road Accidents: Information Extraction from Clinical Reports - SLIDES

Non-original Contributions

  • Linda Cademartori, Giuseppe Galatà , Carola Lo Monaco, Marco Maratea, Marco Mochi and Marco Schouten. An ASP-based Approach to Master Surgical Scheduling - SLIDES

  • Marianna Inglese, Matteo Ferrante, Andrea Duggento, Tommaso Boccato and Nicola Toschi. Spatiotemporal learning of dynamic Positron Emission Tomography data improves diagnostic accuracy in breast cancer - SLIDES

  • Lucia Migliorelli, Francesco Alborino, Lorenzo Scoppolini Massini, Daniele Berardini, Michela Coccia, Laura Villani, Emanuele Frontoni and Sara Moccia. End-to-end facial-landmark detection to assess dysarthria evolution - SLIDES

  • Alessio Bottrighi, Federica Grosso, Marco Ghiglione, Antonio Maconi, Luca Piovesan, Annalisa Roveta and Paolo Terenziani. GLARE-Edu: A CIG-based approach for medical education - SLIDES

  • Marco Colussi, Gabriele Civitarese, Dragan Ahmetovic, Claudio Bettini, Roberta Gualtierotti, Flora Peyvandi and Sergio Mascetti. Ultrasound Detection of Subquadricipital Recess Distension - SLIDES

  • Matteo Ferrante, Tommaso Boccato and Nicola Toschi. BayesNetCNN: incorporating uncertainty in neural networks for image-based classification tasks - SLIDES 1 - SLIDES 2

  • Roberto Zanoli, Alberto Lavelli and Fabio Rinaldi. An annotated dataset for extracting gene-melanoma relations from scientific literature

  • Tommaso Boccato, Matteo Ferrante, Andrea Duggento and Nicola Toschi. Converting Biologically Plausible Networks into Trainable Neural Architectures - SLIDES

  • Davide Colla, Matteo Delsanto, Marco Agosto, Benedetto Vitiello and Daniele P. Radicioni. Semantic Coherence Markers for the Early Diagnosis of the Alzheimer Disease - SLIDES

  • Francesca Pia Villani, Sara Moccia and Emanuele Frontoni. Towards computer-assisted laryngoscopy for diagnostic support

  • Andrea Duggento, Mario de Lorenzo, Stefano Bargione, Allegra Conti, Vincenzo Catrambone, Gaetano Valenza and Nicola Toschi. An Intertwined Neural Network model for EEG classification - SLIDES

  • Andrea Bernardini, Andrea Brunello, Gian Luigi Gigli, Angelo Montanari and Nicola Saccomanno. AIOSA: An approach to the automatic identification of obstructive sleep apnea events based on deep learning - SLIDES

  • Alessandro Cacciatore, Lucia Migliorelli, Daniele Berardini, Simona Tiribelli, Stefano Pigliapoco and Sara Moccia. Some ethical remarks on Deep Learning-based movements monitoring for preterm infants: Green AI or Red AI? - SLIDES