The EfficientUNet++ is an advanced neural network architecture designed for efficient and accurate image segmentation tasks. It combines the decoder architecture inspired on the UNet++ structure with the EfficientNet building blocks to achieve higher performance and lower computational complexity.
UNet++ and EfficientNet building blocks
The UNet++ structure is a popular encoder-decoder architecture used for semantic segmentation tasks. It consists of a series of convolutional and pooling layers that progressively downsample the input image and extract low-level features. The decoder part then upsamples the feature maps and combines them with the corresponding high-level feature maps from the encoder part to generate the final segmentation map.
The EfficientNet building blocks, on the other hand, are a set of efficient convolutional blocks designed for mobile and embedded devices. They consist of a combination of depthwise convolutions and pointwise convolutions, which reduce the computational cost and memory usage of the network without sacrificing accuracy.
EfficientUNet++ modifications
By combining the UNet++ structure with the EfficientNet building blocks, the EfficientUNet++ achieves higher performance and lower computational complexity than the original UNet++ architecture. It does this through two simple modifications:
Residual bottleneck blocks with depthwise convolutions
The first modification is to replace the 3x3 convolutions of the UNet++ with residual bottleneck blocks with depthwise convolutions. These bottleneck blocks consist of a depthwise convolution layer followed by a pointwise convolution layer, which reduces the number of parameters and the computational cost of the network by a significant margin.
Concurrent spatial and channel squeeze & excitation blocks
The second modification is to apply channel and spatial attention to the bottleneck feature maps using concurrent spatial and channel squeeze & excitation (scSE) blocks. The scSE blocks consist of two branches that squeeze the spatial and channel dimensions of the feature maps into a scalar value using average pooling and max pooling, respectively. They then pass the resulting scalar values through two fully connected layers and a sigmoid activation function to generate the attention weights, which are multiplied with the original feature maps to enhance their informative content while suppressing the noise.
Benefits of EfficientUNet++
The EfficientUNet++ architecture has several benefits over the original UNet++ architecture:
Higher performance
The EfficientUNet++ achieves higher performance than the original UNet++ architecture on several benchmark datasets, including PASCAL VOC, Cityscapes, and ISIC 2018 Skin Lesion Segmentation Challenge. This is due to the efficient use of the depthwise convolutions and attention mechanisms, which reduce the overfitting and enhance the discriminability of the feature maps.
Lower computational complexity
The EfficientUNet++ has significantly lower computational complexity than the original UNet++ architecture, which makes it more suitable for deployment on mobile and embedded devices with limited computational resources. This is due to the reduced number of parameters and the efficient use of the depthwise convolutions and attention mechanisms.
Flexibility and versatility
The EfficientUNet++ architecture is flexible and versatile, as it can be easily adapted to various image segmentation tasks and datasets. It can also be combined with other advanced techniques, such as transfer learning, data augmentation, and uncertainty estimation, to further enhance its performance and generalization ability.
The EfficientUNet++ architecture is an advanced neural network architecture designed for efficient and accurate image segmentation tasks. It combines the UNet++ structure with the EfficientNet building blocks and two modifications, including residual bottleneck blocks with depthwise convolutions and concurrent spatial and channel squeeze & excitation blocks, to achieve higher performance and lower computational complexity. It has several benefits over the original UNet++ architecture, including higher performance, lower computational complexity, and flexibility and versatility, which make it more suitable for deployment on mobile and embedded devices and various image segmentation tasks and datasets.
