What is Attention Res-UNet
Attention ResUNet is an advanced neural network architecture for high-precision image segmentation, particularly in medical imaging. It integrates the strengths of UNet’s encoder-decoder structure, ResNet’s residual learning, and attention mechanisms to enhance segmentation accuracy and efficiency. The residual blocks facilitate training deeper networks by maintaining gradient flow, while attention gates focus on relevant image regions, improving feature representation. This combination allows Attention ResUNet to deliver superior performance in tasks like tumour detection, organ segmentation, and retinal vessel segmentation, making it a powerful tool for complex segmentation challenges.
Segment Anything : A Foundation Model for Image Segmentation
In computer vision, segmenting an image into separate segments or regions is a crucial operation. The article “Segment Anything – A Foundation Model for Image Segmentation” provides an introduction to Attention Res-UNet which is an essential model for making separate aspects visible through images.
In this article, we explore the idea of a foundation model designed for image segmentation, which includes its structure and how to execute it in several stages such as data preparation, creation, learning as well as outcome forecasts, also talk about performance evaluation measures of the product and offers some examples for a better understanding of its use across different fields too.
Table of Content
- Overview of Image Segmentation
- What is Attention Res-UNet
- Image Segmentation Stepwise Implementation
- Step 1: Import necessary libraries
- Step 2: Download and Extract Mask Images
- Step 3: Load Mask Images and Image Dataset
- Load Mask Images
- Load Image Dataset
- Step 4: Preprocessing
- Load and Resize Images and Masks
- Display Image and Mask
- Step 5: Model Building
- Define Helper Functions
- Attention block
- Encoder block
- Decoder block
- Define Attention ResUNet Model
- Model Summary
- Step 6: Model Training
- Prepare Data for Training
- Define Callbacks and Compile Model
- Train Model
- Step 7: Predictions
- Helper Functions to Calculate Area
- Predict and Display Results
- Application of Attention Res-UNet in Image Segmentation
- Performance Evaluation and Case Studies of Attention Res-UNet
- Case Studies:
- Conclusion