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Medical image interpretation challenges and research activities of the tAImedIA group at UniBS

Contributo in Atti di convegno
Data di Pubblicazione:
2023
Abstract:
The Trustworthy-AI Medical Image Analysis group at the University of Brescia is a team dedicated to advancing the field of medical image analysis through collaborative research activities. The group's efforts are concentrated on the development of innovative systems and solutions to address complex image interpretation challenges, specifically within two imaging modalities: Brain MRI and Chest X-ray, and their corresponding anatomical districts. The group's research efforts are aimed at improving the accuracy, speed, and efficiency of image interpretation, with a focus on ensuring the reliability and safety of AI-assisted medical decision-making processes. By leveraging advanced deep learning techniques, the group aims to develop cutting-edge algorithms that can accurately and efficiently analyze medical images, aiding in the detection, diagnosis, and treatment of various medical conditions.
Tipologia CRIS:
4.1 Contributo in Atti di convegno
Keywords:
Brain segmentation; Cardiovascular risk factors; Chest X-ray; Cortical thickness; COVID-19 prognosis; Deep learning; Magnetic Resonance Imaging
Elenco autori:
Signoroni, A.; Savardi, M.; Farina, D.; Benini, S.; Coppola, E.; Ferrari, D.; Massussi, M.; Curello, S.; Svanera, M.; D'Ancona, G.
Autori di Ateneo:
BENINI Sergio
FARINA DAVIDE
MASSUSSI MAURO
SAVARDI MATTIA
SIGNORONI ALBERTO
Link alla scheda completa:
https://iris.unibs.it/handle/11379/593145
Titolo del libro:
CEUR Workshop Proceedings
Pubblicato in:
CEUR WORKSHOP PROCEEDINGS
Journal
CEUR WORKSHOP PROCEEDINGS
Series
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