UNet
UNet is a convolutional neural network architecture commonly used for biomedical image segmentation tasks, although it has also found applications in other domains. It was introduced by Ronneberger et al. in 2015 and has since become a popular choice due to its effectiveness in handling tasks where precise pixel-level localization is required.
The UNet architecture consists of a contracting path, which captures context and downsamples the input image, followed by an expansive path, which enables precise localization through upsampling. The contracting path resembles the typical encoder structure seen in convolutional neural networks, while the expansive path involves upsampling and concatenation operations to recover spatial information.
Key components of the UNet architecture include:
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Encoder (Contracting Path): This part of the network consists of multiple convolutional layers followed by downsampling operations such as max-pooling or strided convolutions. The purpose of the encoder is to extract hierarchical features from the input image while reducing spatial dimensions.
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Decoder (Expansive Path): The decoder consists of upsampling layers followed by convolutional layers. It helps in recovering the spatial resolution lost during the encoding stage. Skip connections are employed to concatenate feature maps from the encoder to the corresponding layers in the decoder. These skip connections facilitate the precise localization of objects by providing high-resolution features at multiple scales.
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Skip Connections: These connections between corresponding layers of the encoder and decoder paths help in preserving fine-grained details and gradients during training. By combining features from multiple scales, the network can effectively localize objects even with limited training data.
UNet has been successfully applied to various segmentation tasks, including cell segmentation, organ segmentation in medical images, satellite image segmentation, and more. Its ability to handle limited training data, along with its strong performance in pixel-level localization, makes it a popular choice for many image segmentation applications. Additionally, variations of the UNet architecture have been proposed, such as the Nested UNet and Attention UNet, which incorporate additional components to enhance segmentation accuracy further.