Image Segmentation with Attention for Biomedical Images
Biomedical Image Segmentation: Attention U-Net - Towards Data Science
Improving model sensitivity and accuracy by attaching attention gates on top of the standard U-NetMedical image segmentation has been actively studied to automate clinical analysis. Deep learning models generally require a large amount of data, but acquiring medical images is tedious and error-prone.Attention U-Net aims to automatically learn to focus on target structures of varying shapes and sizes; thus, the name of the paper “learning where to look for the Pancreas” by Oktay et al.Related works before Attention U-NetU-NetU-Nets are commonly used for image segmentation tasks because of its performance and efficient use of GPU memory. It aims to achieve high precision that is reliable for clinical usage with fewer training samples because acquiring annotated medical images can be resource-intensive. Read more about U-Net.Despite U-Net excellent representation capability, it relies on multi-stage cascaded convolutional neural networks to work. These cascaded frameworks…