Contrast-Informed Augmentation and Domain-Adversarial Training for Adult-to-Neonatal MR Reconstruction Generalization
arXiv:2606.13562v1 Announce Type: cross Abstract: Purpose: To investigate whether contrast-informed data augmentation and domain-adversarial training improve the adult-to-neonatal generalization of the E2E-VarNet. Methods: Three training regimes were investigated: (1) adult-only training with unaugmented adult data, (2) mixed training with paired unaugmented and neonatal-informed augmented adult data, and (3) mixed training with a domain-adversarial objective. Models were trained on retrospectively undersampled multi-coil adult T2-weighted brain MR data and evaluated on neonatal and adult test
The continuous advancements in AI and medical imaging create opportunities for more robust and generalizable diagnostic tools.
Improving the generalizability of AI models in medical imaging, especially across different patient populations, is crucial for wider clinical adoption and equitable healthcare.
This research enhances the reliability of AI-driven MRI reconstruction for varied demographics, potentially reducing the need for extensive retraining and improving diagnostic consistency.
- · Medical AI developers
- · Healthcare providers
- · Neonatal care
- · Patients needing MRI scans
More accurate and versatile AI models for medical imaging become available.
Reduced barriers to deploying AI in diverse clinical settings, including pediatric and neonatal units.
Accelerated development of AI-driven diagnostic tools, potentially shifting diagnostic workflows and expertise requirements.
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Read at arXiv cs.AI