ozan-oktay/Attention-Gated-Networks

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2064 stars · 436 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch implementation of attention gates for convolutional neural networks, applied to U-Net and VGG-16 architectures. It targets medical image classification and segmentation tasks, as described in the authors' MIDL 2018 papers.

Use cases

• add attention gates to a U-Net segmentation model
• train a CNN for medical image segmentation
• implement Attention U-Net for pancreas segmentation
• classify ultrasound scan planes with attention-gated networks
• reproduce results from the Attention U-Net paper
• improve CNN interpretability with attention maps

When to choose

• you need a reference PyTorch implementation of attention gates for medical imaging
• you want to reproduce or extend the Attention U-Net or Sononet papers
• you are doing research on attention mechanisms in CNNs

When to avoid

• you need a maintained, production-ready segmentation framework
• you want a general-purpose attention/transformer library
• you need support for frameworks other than PyTorch

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision deep-learning computer-vision healthcare image-processing python pytorch attention-gates unet medical-imaging image-segmentation image-classification research-code

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