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Research questionHow can automated quality control reliably grade artifact severity in ultra-low-field neonatal brain MRI with practical inference costs?Low signal-to-noise ratio, absent shielding, and long scans make ultra-low-field neonatal brain MRI vulnerable to acquisition artifacts. Quality control must distinguish severity across several artifact types while remaining practical to deploy.
AI
Computer Vision
Evaluation & Benchmarks
Health
Image & Video Processing
Machine Learning
Research Paper
Small / On-device Models
Latest papersRecent research connected to this question, newest first.LoFi RADIO: A Distilled In-Domain Backbone Applied for Artifact-Severity Grading of Ultra-Low-Field Neonatal Brain MRThe evidence concerns the LISA 2026 Task 1a task: grading seven artifacts on 0/1/2 severity scales in ultra-low-field T2-weighted neonatal brain MRI volumes. It evaluates classification backbones, per-artifact routing among foundation-model teachers, and a distilled in-domain ViT-S student trained with an unlabeled low-field MRI corpus; reported comparisons use a weighted composite and inference requirements.research paper · Sep 2, 2026
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