Stoklasa, Roman - Search Results - CERN Document Server

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Brain MRI Screening Tool with Federated Learning / Stoklasa, Roman (CERN ; Masaryk U., Brno (main)) ; Stathopoulos, Ioannis (CERN ; Athens Natl. Capodistrian U.) ; Karavasilis, Efstratios (Democritus U., Thrace) ; Efstathopoulos, Efstathios (Athens Natl. Capodistrian U.) ; Dostál, Marek (Masaryk U., Brno (main)) ; Keřkovský, Miloš (Masaryk U., Brno (main)) ; Kozubek, Michal (Masaryk U., Brno (main)) ; Serio, Luigi (CERN)
In clinical practice, we often see significant delays between MRI scans and the diagnosis made by radiologists, even for severe cases. [...]
2024 - 5.

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Exploring Multi-Pathology Brain Segmentation: From Volume-Based to Component-Based Deep Learning Analysis / Stathopoulos, Ioannis (CERN ; Athens Natl. Capodistrian U.) ; Stoklasa, Roman (Athens Natl. Capodistrian U. ; Masaryk U., Brno) ; Kouri, Maria Anthi (Athens Natl. Capodistrian U.) ; Velonakis, Georgios (Athens Natl. Capodistrian U.) ; Karavasilis, Efstratios (Democritus U., Thrace) ; Efstathopoulos, Efstathios (Athens Natl. Capodistrian U.) ; Serio, Luigi (Athens Natl. Capodistrian U.)
Detection and segmentation of brain abnormalities using Magnetic Resonance Imaging (MRI) is an important task that, nowadays, the role of AI algorithms as supporting tools is well established both at the research and clinical-production level. While the performance of the state-of-the-art models is increasing, reaching radiologists and other experts’ accuracy levels in many cases, there is still a lot of research needed on the direction of in-depth and transparent evaluation of the correct results and failures, especially in relation to important aspects of the radiological practice: abnormality position, intensity level, and volume. [...]
2024 - 17 p. - Published in : J. Imaging 11 (2024) 6 Fulltext: PDF;

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Decentralized Federated Learning for Healthcare Networks: A Case Study on Tumor Segmentation / Camajori Tedeschini, Bernardo (Milan, Polytech.) ; Savazzi, Stefano (ISTP, Milan) ; Stoklasa, Roman (CERN) ; Barbieri, Luca (Milan, Polytech.) ; Stathopoulos, Ioannis (CERN) ; Nicoli, Monica (Milan, Polytech.) ; Serio, Luigi (CERN)
Smart healthcare relies on artificial intelligence (AI) functions for learning and analysis of patient data. Since large and diverse datasets for training of Machine Learning (ML) models can rarely be found in individual medical centers, classical centralized AI requires moving privacy-sensitive data from medical institutions to data centers that process the fused information. [...]
2022 - 16 p. - Published in : IEEE Access 10 (2022) 8693-8708 Fulltext: PDF;

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