RobMedNAS: searching robust neural network architectures for medical image synthesis.
Biomed Phys Eng Express
; 10(5)2024 Aug 23.
Article
in En
| MEDLINE
| ID: mdl-39137798
ABSTRACT
Investigating U-Net model robustness in medical image synthesis against adversarial perturbations, this study introduces RobMedNAS, a neural architecture search strategy for identifying resilient U-Net configurations. Through retrospective analysis of synthesized CT from MRI data, employing Dice coefficient and mean absolute error metrics across critical anatomical areas, the study evaluates traditional U-Net models and RobMedNAS-optimized models under adversarial attacks. Findings demonstrate RobMedNAS's efficacy in enhancing U-Net resilience without compromising on accuracy, proposing a novel pathway for robust medical image processing.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Image Processing, Computer-Assisted
/
Magnetic Resonance Imaging
/
Tomography, X-Ray Computed
/
Neural Networks, Computer
Limits:
Humans
Language:
En
Journal:
Biomed Phys Eng Express
Year:
2024
Document type:
Article
Affiliation country:
United States