# Attention-Gated Networks for Improving Ultrasound Scan Plane Detection

arXiv: https://arxiv.org/abs/1804.05338 · HTML: https://mlsanity.com/paper/1804.05338/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2018.

## Built on by (most cited first, 22 of 22)

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- [Anatomy-Aware Contrastive Representation Learning for Fetal Ultrasound](https://mlsanity.com/paper/2208.10642/) (2022)
- [Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound](https://mlsanity.com/paper/2105.10626/) (2021)
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- [MCAT: Visual Query-Based Localization of Standard Anatomical Clips in Fetal Ultrasound Videos Using Multi-Tier Class-Aware Token Transformer](https://mlsanity.com/paper/2504.06088/) (2025)
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- [Multi-Attention Stacked Ensemble for Lung Cancer Detection in CT Scans](https://mlsanity.com/paper/2507.20221/) (2025)
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## Abstract (arXiv)

In this work, we apply an attention-gated network to real-time automated scan plane detection for fetal ultrasound screening. Scan plane detection in fetal ultrasound is a challenging problem due the poor image quality resulting in low interpretability for both clinicians and automated algorithms. To solve this, we propose incorporating self-gated soft-attention mechanisms. A soft-attention mechanism generates a gating signal that is end-to-end trainable, which allows the network to contextualise local information useful for prediction. The proposed attention mechanism is generic and it can be easily incorporated into any existing classification architectures, while only requiring a few additional parameters. We show that, when the base network has a high capacity, the incorporated attention mechanism can provide efficient object localisation while improving the overall performance. When the base network has a low capacity, the method greatly outperforms the baseline approach and significantly reduces false positives. Lastly, the generated attention maps allow us to understand the model's reasoning process, which can also be used for weakly supervised object localisation.