Keywords: Analysis/Processing, AI/ML Software, Cardiovascular, Quantitative Imaging, Myocardium
Motivation: Training robust convolutional neural networks (CNNs) for cardiac magnetic resonance (CMR) imaging is essential, as varying imaging protocols and patient demographics can significantly affect performance.
Goal(s): The aim is to develop an evolutionary algorithm that computes learning environments (LEs) for CNNs, enhancing their adaptability for myocardial segmentation and reference point estimation in T2 parametric mapping.
Approach: The algorithm trains 48 CNNs simultaneously, adjusting LEs through mutation based on performance evaluations.
Results: Performance improved over time, achieving a Dice score of 86% and a reference point distance of 6mm, indicating effective adaptability across varying cardiac geometries and pixel intensities.
Impact: The evolutionary algorithm improves CNN performance in cardiovascular MRI by dynamically optimizing learning environments, enhancing adaptability across varied imaging conditions. This approach strengthens model generalizability and reliability, making it a promising method for advancing robust AI tools in clinical practice.
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