Medical image segmentation with PyTorch and U-Net
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Learn how to perform medical image segmentation using PyTorch and U-Net. Understand how to apply deep learning techniques to segment medical images, improving diagnosis accuracy in healthcare applications.
At a Glance
Explore biomedical image segmentation in computer vision with PyTorch and U-Net, an architecture also used in Stable Diffusion. Learn to construct a U-Net model using PyTorch’s convolutional, max-pooling, and upsampling layers. Through hands-on steps, train your model to automate image segmentation, showcasing the power of deep learning in medical imaging. Perfect for biomedical engineering or data science professionals aiming to enhance diagnostic accuracy.
- Understand the basics of biomedical image segmentation: Learn the importance and applications of image segmentation in the medical field.
- Construct a U-Net model using PyTorch: Learn to build a U-Net architecture using PyTorch’s convolutional, max-pooling, and upsampling layers.
- Train and evaluate your model for accurate segmentation: Develop the ability to train your model and assess its performance in automating image segmentation tasks.
What you’ll need
- Basic knowledge of Python and PyTorch: Understanding of Python programming and basic PyTorch operations is required.
- Familiarity with deep learning concepts: Some knowledge of neural networks and deep learning principles will be beneficial.
- IBM Skills Network Labs environment: This project uses the IBM Skills Network Labs environment, which comes with many tools pre-installed, including Docker.
- Current versions of web browsers: This platform works best with the latest versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.
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