# Attention-Gated Convolutional Networks for Scanner-Agnostic Quality Assessment

arXiv: https://arxiv.org/abs/2604.15059 · HTML: https://mlsanity.com/paper/2604.15059/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 16 Apr 2026.

## Builds on (most cited first, 7 of 7)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [CoAtNet: Marrying Convolution and Attention for All Data Sizes](https://mlsanity.com/paper/2106.04803/) (2021)
- [Automated detection of motion artifacts in brain MR images using deep learning and explainable artificial intelligence](https://mlsanity.com/paper/2402.08749/) (2024)
- [Estimation of 3T MR images from 1.5T images regularized with Physics based Constraint](https://mlsanity.com/paper/2501.01464/) (2024)
- [Interpretable Motion Artificat Detection in structural Brain MRI](https://mlsanity.com/paper/2603.05726/) (2026)
- [Quality assessment of brain structural MR images: Comparing generalization of deep learning versus hand-crafted feature-based machine learning methods to new sites](https://mlsanity.com/paper/2603.18050/) (2026)

## Abstract (arXiv)

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. Our architecture integrates a hierarchical 2D CNN encoder for local spatial feature extraction with a multi-head cross-attention mechanism to model global dependencies. This synergy enables the model to prioritize motion relevant artifact signatures, such as ringing and blurring, while dynamically filtering out site-specific intensity variations and background noise. The framework was trained end-to-end on the MR-ART dataset using a balanced cohort of 200 subjects. Performance was evaluated across two tiers: Seen Site Evaluation on a held-out MR-ART partition and Unseen Site Evaluation using 200 subjects from 17 heterogeneous sites in the ABIDE archive. On seen sites, the model achieved a scan-level accuracy of 0.9920 and an F1-score of 0.9919. Crucially, it maintained strong generalization across unseen ABIDE sites (Acc = 0.755) without any retraining or fine-tuning, demonstrating high resilience to domain shift. These results indicate that attention-based feature re-weighting successfully captures universal artifact descriptors, bridging the performance gap between diverse imaging environments and scanner manufacturers.