AI RESEARCH
Attention-Gated Convolutional Networks for Scanner-Agnostic Quality Assessment
arXiv CS.CV
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ArXi:2604.15059v1 Announce Type: new Motion artifacts present a significant challenge in structural MRI (sMRI), often compromising clinical diagnostics and large-scale automated analysis. While manual quality control (QC) remains the gold standard, it is increasingly unscalable for massive longitudinal studies. To address this, we propose a hybrid CNN-Attention framework designed for robust, site-invariant MRI quality assessment.