Detailed Information on Publication Record
2024
Brain MRI Screening Tool with Federated Learning
STOKLASA, Roman, Ioannis STATHOPOULOS, Efstratios KARAVASILIS, Efstathios EFSTATHOPOULOS, Marek DOSTÁL et. al.Basic information
Original name
Brain MRI Screening Tool with Federated Learning
Authors
STOKLASA, Roman (703 Slovakia, guarantor, belonging to the institution), Ioannis STATHOPOULOS, Efstratios KARAVASILIS, Efstathios EFSTATHOPOULOS, Marek DOSTÁL (203 Czech Republic), Miloš KEŘKOVSKÝ (203 Czech Republic), Michal KOZUBEK (203 Czech Republic) and Luigi SERIO
Edition
Athens, Greece, 2024 IEEE International Symposium on Biomedical Imaging, p. 1-5, 5 pp. 2024
Publisher
IEEE
Other information
Language
English
Type of outcome
Stať ve sborníku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
United States of America
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
electronic version available online
Organization unit
Faculty of Informatics
ISBN
979-8-3503-1333-8
Keywords in English
MRI; brain; tumor; screening; FL; federated; learning
Tags
Tags
International impact, Reviewed
Změněno: 12/9/2024 15:20, RNDr. Roman Stoklasa, Ph.D.
Abstract
V originále
In clinical practice, we often see significant delays between MRI scans and the diagnosis made by radiologists, even for severe cases. In some cases, this may be caused by the lack of additional information and clues, so even the severe cases need to wait in the queue for diagnosis. This can be avoided if there is an automatic software tool, which would supplement additional information, alerting radiologists that the particular patient may be a severe case. We are presenting an automatic brain MRI Screening Tool and we are demonstrating its capabilities for detecting tumor-like pathologies. It is the first version on the path toward a robust multi-pathology screening solution. The tool supports Federated Learning, so multiple institutions may contribute to the model without disclosing their private data. The tool detected 98% of brain tumors in our testing dataset (102 patients) with a precision of 91 %, achieving a segmentation Dice score more than 0.88.
Links
LM2023050, research and development project |
| ||
NU21-08-00359, research and development project |
|