MicroNet

tiatoolbox . models . architecture . micronet . MicroNet

class MicroNet(num_input_channels=3, num_output_channels=2, out_activation='softmax')

Initialize MicroNet [1].

The following models have been included in tiatoolbox:

1. micronet-consep:

   This is trained on CoNSeP dataset The model is retrained in torch as the original model with results on CoNSeP [2] was trained in TensorFlow.

The tiatoolbox model should produce the following results on the CoNSeP dataset:

MicroNet performance

Model name	Data set	DICE	AJI	DQ	SQ	PQ
micronet-consep	CoNSeP	0.80	0.49	0.62	0.75	0.47

Parameters:

• num_input_channels (int) – Number of channels in input. default=3.

• num_output_channels (int) – Number of output channels. default=2.

• out_activation (str) – Activation to use at the output. MapDe inherits MicroNet but uses ReLU activation.

References

[1] Raza, Shan E Ahmed, et al. “Micro-Net: A unified model for segmentation of various objects in microscopy images.” Medical image analysis 52 (2019): 160-173.

[2] Graham, Simon, et al. “Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images.” Medical Image Analysis 58 (2019): 101563.

Initialize MicroNet.

Methods

forward	Logic for using layers defined in init.
infer_batch	Run inference on an input batch.
postproc	Post-processing script for MicroNet.
preproc	Preprocessing function for MicroNet.

Attributes

training

forward(input_tensor)

Logic for using layers defined in init.

This method defines how layers are used in forward operation.

Parameters:

• input_tensor (torch.Tensor) – Input images, the tensor is in the shape of NCHW.

• self (MicroNet)

Returns:

A list of main and auxiliary outputs. The expected format is [main_output, aux1, aux2, aux3].

Return type:

list

static infer_batch(model, batch_data, *, on_gpu)

Run inference on an input batch.

This contains logic for forward operation as well as batch I/O aggregation.

Parameters:

• model (nn.Module) – PyTorch defined model.

• batch_data (torch.Tensor) – A batch of data generated by torch.utils.data.DataLoader.

• on_gpu (bool) – Whether to run inference on a GPU.

Returns:

Probability map as a numpy array.

Return type:

list(np.ndarray)

static postproc(image)

Post-processing script for MicroNet.

Parameters:

image (ndarray) – Input image of type numpy array.

Returns:

Pixel-wise nuclear instance segmentation prediction.

Return type:

numpy.ndarray

static preproc(image)

Preprocessing function for MicroNet.

Performs per image standardization.

Parameters:

image (numpy.ndarray) – Input image of type numpy array.

Returns:

Pre-processed numpy array.

Return type:

numpy.ndarray