Program and Accepted Papers

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