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What is U-Net?

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U-Net is a convolutional neural network that was developed for biomedical image segmentation. The network is based on a fully convolutional network whose architecture was modified and extended to work with fewer training images and yield more precise segmentation.

U-Net was first proposed in a research paper published in 2015.

Architecture

U-net architecture is symmetric and consists of two major parts: * The left part is called the contracting path, constituted by the general convolutional process.

  • The right part is an expansive path, constituted by transposed 2D convolutional layers.
U-net architecture (example for 32x32 pixels in the lowest resolution). Each bluebox corresponds to a multi-channel feature map.
U-net architecture (example for 32x32 pixels in the lowest resolution). Each bluebox corresponds to a multi-channel feature map.

The image is input at the beginning of the network (left-top). The data is then propagated through all possible paths and, in the end, the segmentation map comes out.

The main idea is to supplement a usual contracting network by successive layers, where upsampling operators replace pooling operations. Hence these layers increase the resolution of the output. What’s more, a successive convolutional layer can then learn to assemble a precise output based on this information.

Key features

  • U-Net learns segmentation in an end-to-end setting.
    You input a raw image and get a segmentation map as the output.

  • U-Net is able to precisely localize and distinguish borders.
    Performs classification on every pixel so that the input and output share the same size.

  • U-Net uses very few annotated images.
    Data augmentation with elastic deformations reduces the number of annotated images required for training.

You can find the U-net implementation, trained networks, and supplementary material here.

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machine learning
definition
deep learning
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Grokking Modern System Design Interview for Engineers & Managers

Ace your System Design Interview and take your career to the next level. Learn to handle the design of applications like Netflix, Quora, Facebook, Uber, and many more in a 45-min interview. Learn the RESHADED framework for architecting web-scale applications by determining requirements, constraints, and assumptions before diving into a step-by-step design process.

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