Pretrained Neural Network Models¶
Patch Classification¶
Kather Patch Dataset¶
The following models are trained using Kather Dataset.
They share the same input output configuration defined below:
Input Output Configuration Details
from tiatoolbox.models import IOPatchPredictorConfig
ioconfig = IOPatchPredictorConfig(
patch_input_shape=(224, 224),
stride_shape=(224, 224),
input_resolutions=[{"resolution": 0.5, "units": "mpp"}]
)
Model names
alexnet-kather100k
resnet18-kather100k
resnet34-kather100k
resnet50-kather100k
resnet101-kather100k
resnext50_32x4d-kather100k
resnext101_32x8d-kather100k
wide_resnet50_2-kather100k
wide_resnet101_2-kather100k
densenet121-kather100k
densenet161-kather100k
densenet169-kather100k
densenet201-kather100k
mobilenet_v2-kather100k
mobilenet_v3_large-kather100k
mobilenet_v3_small-kather100k
googlenet-kather100k
Patch Camelyon (PCam) Dataset¶
The following models are trained using the PCam dataset. They share the same input output configuration defined below:
Input Output Configuration Details
from tiatoolbox.models import IOPatchPredictorConfig
ioconfig = IOPatchPredictorConfig(
patch_input_shape=(96, 96),
stride_shape=(96, 96),
input_resolutions=[{"resolution": 1.0, "units": "mpp"}]
)
Model names
alexnet-pcam
resnet18-pcam
resnet34-pcam
resnet50-pcam
resnet101-pcam
resnext50-pcam
resnext101-pcam
wide_resnet50_2-pcam
wide_resnet101_2-pcam
densenet121-pcam
densenet161-pcam
densenet169-pcam
densenet201-pcam
mobilenet_v2-pcam
mobilenet_v3_large-pcam
mobilenet_v3_small-pcam
googlenet-pcam
Semantic Segmentation¶
Tissue Masking¶
The following models are trained using internal data of TIA Centre. They share the same input output configuration defined below:
Input Output Configuration Details
from tiatoolbox.models import IOSegmentorConfig
ioconfig = IOSegmentorConfig(
input_resolutions=[
{'units': 'mpp', 'resolution': 2.0}
],
output_resolutions=[
{'units': 'mpp', 'resolution': 2.0}
],
patch_input_shape=(1024, 1024),
patch_output_shape=(512, 512),
stride_shape=(256, 256),
save_resolution={'units': 'mpp', 'resolution': 8.0}
)
Model names
fcn-tissue_mask
Breast Cancer¶
The following models are trained using the BCSS dataset. They share the same input output configuration defined below:
Input Output Configuration Details
from tiatoolbox.models import IOSegmentorConfig
ioconfig = IOSegmentorConfig(
input_resolutions=[
{'units': 'mpp', 'resolution': 0.25}
],
output_resolutions=[
{'units': 'mpp', 'resolution': 0.25}
],
patch_input_shape=(1024, 1024),
patch_output_shape=(512, 512),
stride_shape=(256, 256),
save_resolution={'units': 'mpp', 'resolution': 0.25}
)
Model names
fcn_resnet50_unet-bcss
Nucleus Instance Segmentation¶
PanNuke Dataset¶
We provide the following models trained using the PanNuke dataset, which uses the following input output configuration:
Input Output Configuration Details
from tiatoolbox.models import IOSegmentorConfig
ioconfig = IOSegmentorConfig(
input_resolutions=[
{'units': 'mpp', 'resolution': 0.25}
],
output_resolutions=[
{'units': 'mpp', 'resolution': 0.25},
{'units': 'mpp', 'resolution': 0.25},
{'units': 'mpp', 'resolution': 0.25}
],
margin=128
tile_shape=[1024, 1024]
patch_input_shape=(256, 256),
patch_output_shape=(164, 164),
stride_shape=(164, 164),
save_resolution={'units': 'mpp', 'resolution': 0.25}
)
Model names
hovernet_fast-pannuke
MoNuSAC Dataset¶
We provide the following models trained using the MoNuSAC dataset, which uses the following input output configuration:
Input Output Configuration Details
from tiatoolbox.models import IOSegmentorConfig
ioconfig = IOSegmentorConfig(
input_resolutions=[
{'units': 'mpp', 'resolution': 0.25}
],
output_resolutions=[
{'units': 'mpp', 'resolution': 0.25},
{'units': 'mpp', 'resolution': 0.25},
{'units': 'mpp', 'resolution': 0.25}
],
margin=128
tile_shape=[1024, 1024]
patch_input_shape=(256, 256),
patch_output_shape=(164, 164),
stride_shape=(164, 164),
save_resolution={'units': 'mpp', 'resolution': 0.25}
)
Model names
hovernet_fast-monusac
CoNSeP Dataset¶
We provide the following models trained using the CoNSeP dataset, which uses the following input output configuration:
Input Output Configuration Details
from tiatoolbox.models import IOSegmentorConfig
ioconfig = IOSegmentorConfig(
input_resolutions=[
{'units': 'mpp', 'resolution': 0.25}
],
output_resolutions=[
{'units': 'mpp', 'resolution': 0.25},
{'units': 'mpp', 'resolution': 0.25},
{'units': 'mpp', 'resolution': 0.25}
],
margin=128
tile_shape=[1024, 1024]
patch_input_shape=(270, 270),
patch_output_shape=(80, 80),
stride_shape=(80, 80),
save_resolution={'units': 'mpp', 'resolution': 0.25}
)
Model names
hovernet_original-consep
micronet_hovernet-consep
Kumar Dataset¶
We provide the following models trained using the Kumar dataset, which uses the following input output configuration:
Input Output Configuration Details
from tiatoolbox.models import IOSegmentorConfig
ioconfig = IOSegmentorConfig(
input_resolutions=[
{'units': 'mpp', 'resolution': 0.25}
],
output_resolutions=[
{'units': 'mpp', 'resolution': 0.25},
{'units': 'mpp', 'resolution': 0.25},
{'units': 'mpp', 'resolution': 0.25}
],
margin=128
tile_shape=[1024, 1024]
patch_input_shape=(270, 270),
patch_output_shape=(80, 80),
stride_shape=(80, 80),
save_resolution={'units': 'mpp', 'resolution': 0.25}
)
Model names
hovernet_original_kumar
Multi-Task Segmentation¶
We provide the following model trained using a private OED dataset. The model outputs nuclear instance segmentation and classification results, as well as semantic segmentation of epithelial layers. The model uses the following input output configuration:
Input Output Configuration Details
from tiatoolbox.models import IOSegmentorConfig
ioconfig = IOSegmentorConfig(
input_resolutions=[
{'units': 'mpp', 'resolution': 0.5}
],
output_resolutions=[
{'units': 'mpp', 'resolution': 0.5},
{'units': 'mpp', 'resolution': 0.5},
{'units': 'mpp', 'resolution': 0.5},
{'units': 'mpp', 'resolution': 0.5}
],
margin=128
tile_shape=[1024, 1024]
patch_input_shape=(256, 256),
patch_output_shape=(164, 164),
stride_shape=(164, 164),
save_resolution={'units': 'mpp', 'resolution': 0.5}
)
Model names
hovernetplus-oed