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In this work, we apply an attention-gated network to real-time automated scanplane detection for fetal ultrasound screening. Scan plane detection in fetalultrasound is a challenging problem due the poor image quality resulting in lowinterpretability for both clinicians and automated algorithms. To solve this,we propose incorporating self-gated soft-attention mechanisms. A soft-attentionmechanism generates a gating signal that is end-to-end trainable, which allowsthe network to contextualise local information useful for prediction. Theproposed attention mechanism is generic and it can be easily incorporated intoany existing classification architectures, while only requiring a fewadditional parameters. We show that, when the base network has a high capacity,the incorporated attention mechanism can provide efficient object localisationwhile improving the overall performance. When the base network has a lowcapacity, the method greatly outperforms the baseline approach andsignificantly reduces false positives. Lastly, the generated attention mapsallow us to understand the model's reasoning process, which can also be usedfor weakly supervised object localisation.

Attention-Gated Networks for Improving Ultrasound Scan Plane Detection