efficientunet_tissue_mask_model¶

Tissue Mask Segmentation Model Architecture.

This module defines a tissue segmentation model based on an EfficientNet-UNet architecture for identifying tissue regions in digital pathology images. The model implements an EfficientNetB0 encoder with a UNet-style decoder and segmentation head for high-resolution tissue segmentation.

Key Components:¶

  • Conv2dStaticSamePadding:

    Convolutional layer with static same padding.

  • MBConvBlock:

    Mobile Inverted Residual Bottleneck block.

  • EfficientNetEncoder:

    EfficientNetB0 encoder for feature extraction.

  • Conv2dReLU:

    Convolutional block with BatchNorm and ReLU activation.

  • UnetDecoderBlock:

    Decoder block with skip connections for feature fusion.

  • UnetDecoder:

    Decoder with skip connections for UNet architecture.

  • SegmentationHead:

    Final layer for segmentation output.

  • EfficientUNetTissueMaskModel:

    Main model class implementing encoder-decoder architecture for tissue detection.

Features:¶

  • ImageNet normalization during preprocessing.

  • Morphological postprocessing for generating clean tissue masks.

  • Efficient inference pipeline for batch processing.

Example

>>> from tiatoolbox.models.engine.semantic_segmentor import SemanticSegmentor
>>> segmentor = SemanticSegmentor(model="efficientunet-tissue_mask")
>>> results = segmentor.run(
...     ["/example_wsi.svs"],
...     masks=None,
...     auto_get_mask=False,
...     patch_mode=False,
...     save_dir=Path("/tissue_mask/"),
...     output_type="annotationstore",
... )

Classes

Conv2dReLU

Conv2d + BatchNorm + ReLU block.

Conv2dStaticSamePadding

2D Convolution with static same padding.

EfficientNetEncoder

EfficientNetB0 encoder for feature extraction.

EfficientUNetTissueMaskModel

EfficientNet-UNet Tissue Segmentation Model.

MBConvBlock

Mobile Inverted Residual Bottleneck block.

SegmentationHead

Segmentation head for UNet architecture.

UnetDecoder

UNet decoder with skip connections.

UnetDecoderBlock

Decoder block for UNet architecture.