Attention gated networks: Learning to leverage salient regions in medical images

[Display omitted] We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features...

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Veröffentlicht in:	Medical image analysis 2019-04, Vol.53, p.197-207
Hauptverfasser:	Schlemper, Jo, Oktay, Ozan, Schaap, Michiel, Heinrich, Mattias, Kainz, Bernhard, Glocker, Ben, Rueckert, Daniel
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Annotations Artificial neural networks Attention gates Classification Computer applications Computing time Datasets Datasets as Topic Female Fetuses Fully convolutional networks Humans Image analysis Image classification Image Interpretation, Computer-Assisted - methods Image processing Image segmentation Imaging, Three-Dimensional - methods Localisation Localization Mathematical models Medical imaging Model accuracy Neural networks Neural Networks, Computer Organs Performance prediction Pregnancy Radiography, Abdominal - methods Segmentation Sensitivity analysis Soft attention Source code Tomography, X-Ray Computed - methods Ultrasonography, Prenatal - methods Ultrasound
Online-Zugang:	Volltext
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