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X-WR-CALNAME;VALUE=TEXT:Eventi DIAG
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DTSTART:20251026T030000
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UID:calendar.29959.field_data.0@www.u-gov-ricerca.uniroma1.it
DTSTAMP:20260404T191337Z
CREATED:20250919T162717Z
DESCRIPTION:AbstractRecent advances in deep learning have brought remarkabl
 e progress in medical image and video analysis\, but challenges remain in 
 terms of adaptability\, real-time performance\, and interpretability. In t
 his talk\, I will present my research on probabilistic and prototype-based
  approaches to computer vision in the medical domain. First\, I will intro
 duce PrAViC (Probabilistic Adaptation Framework for Real-Time Video Classi
 fication)\, a method designed to improve robustness and adaptability in vi
 deo-based diagnostic tasks. Next\, I will discuss EPIC (Explanation of Pre
 trained Image Classification Networks via Prototypes)\, an interpretabilit
 y framework that leverages prototypes to provide human-understandable expl
 anations of model predictions. Finally\, I will briefly highlight my curre
 nt work with Prometheus MedTech AI\, focusing on translational application
 s of explainable machine learning in clinical settings\, particularly in t
 he diagnosis and management of congenital heart defects.Short BioMagdalena
  Trędowicz is a 2nd year PhD student at the Jagiellonian University (GMUM 
 Team)\, supervised by prof. Łukasz Struski and prof. Jacek Tabor. Her rese
 arch focuses on computer vision\, medical imaging\, and interpretable AI. 
 She is currently involved in the Prometheus MedTech AI project\, where she
  works on developing trustworthy and clinically applicable machine learnin
 g solutions.  
DTSTART;TZID=Europe/Paris:20250925T143000
DTEND;TZID=Europe/Paris:20250925T143000
LAST-MODIFIED:20250919T171048Z
LOCATION:Aula B101\, DIAG
SUMMARY:Bridging Probabilistic Models and Interpretability in Medical Video
  and Image Analysis - Magdalena Trędowicz
URL;TYPE=URI:http://www.u-gov-ricerca.uniroma1.it/node/29959
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