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# dicePixelClassificationLayer

Create pixel classification layer using generalized Dice loss for semantic segmentation

## Description

A Dice pixel classification layer provides a categorical label for each image pixel or voxel using generalized Dice loss.

The layer uses generalized Dice loss to alleviate the problem of class imbalance in semantic segmentation problems. Generalized Dice loss controls the contribution that each class makes to the loss by weighting classes by the inverse size of the expected region.

## Creation

### Syntax

``layer = dicePixelClassificationLayer``
``layer = dicePixelClassificationLayer(Name,Value)``

### Description

example

````layer = dicePixelClassificationLayer` creates a Dice pixel classification output layer for semantic image segmentation networks. The layer outputs the categorical label for each image pixel or voxel processed by a CNN. The layer automatically ignores undefined pixel labels during training.```
````layer = dicePixelClassificationLayer(Name,Value)` returns a Dice pixel classification output layer using Name,Value pair arguments to set the optional `Classes` and `Name` properties. You can specify multiple name-value pairs. Enclose each property name in quotes.For example, `dicePixelClassificationLayer('Name','pixclass')` creates a Dice pixel classification layer with the name `'pixclass'`.```

## Properties

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Classes of the output layer, specified as a categorical vector, string array, cell array of character vectors, or `'auto'`. If `Classes` is `'auto'`, then the software automatically sets the classes at training time. If you specify the string array or cell array of character vectors `str`, then the software sets the classes of the output layer to `categorical(str,str)`.

Data Types: `char` | `categorical` | `string` | `cell`

This property is read-only.

The output size of the layer. The value is `'auto'` prior to training, and is specified as a numeric value at training time.

This property is read-only.

Loss function used for training, specified as `'generalizedDiceLoss'`.

Layer name, specified as a character vector or a string scalar. For `Layer` array input, the `trainNetwork`, `assembleNetwork`, `layerGraph`, and `dlnetwork` functions automatically assign names to layers with `Name` set to `''`.

Data Types: `char` | `string`

This property is read-only.

Number of inputs of the layer. This layer accepts a single input only.

Data Types: `double`

This property is read-only.

Input names of the layer. This layer accepts a single input only.

Data Types: `cell`

## Examples

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Predict the categorical label of every pixel in an input image using a generalized Dice loss function.

```layers = [ imageInputLayer([480 640 3]) convolution2dLayer(3,16,'Stride',2,'Padding',1) reluLayer transposedConv2dLayer(2,4,'Stride',2) softmaxLayer dicePixelClassificationLayer ] ```
```layers = 6x1 Layer array with layers: 1 '' Image Input 480x640x3 images with 'zerocenter' normalization 2 '' Convolution 16 3x3 convolutions with stride [2 2] and padding [1 1 1 1] 3 '' ReLU ReLU 4 '' Transposed Convolution 4 2x2 transposed convolutions with stride [2 2] and cropping [0 0 0 0] 5 '' Softmax softmax 6 '' Dice Pixel Classification Layer Generalized Dice loss ```

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## References

[1] Crum, William R., Oscar Camara, and Derek LG Hill. "Generalized overlap measures for evaluation and validation in medical image analysis." IEEE Transactions on Medical Imaging. 25.11, 2006, pp. 1451–1461.

[2] Sudre, Carole H., et al. "Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations." Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer, Cham, 2017, pp. 240–248.

[3] Milletari, Fausto, Nassir Navab, and Seyed-Ahmad Ahmadi. "V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation". Fourth International Conference on 3D Vision (3DV). Stanford, CA, 2016: pp. 565–571.

## Extended Capabilities

Introduced in R2019b

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