IDaRS#
tiatoolbox
.models
.architecture
.idars
.IDaRS
- class IDaRS(backbone, num_classes=1)[source]#
Initialise IDaRS and add custom preprocessing as used in the original paper [1].
The tiatoolbox model should produce the following results:
# MSI
TP53
BRAF
CIMP
CIN
HM
Bilal et al.
0.828
0.755
0.813
0.853
0.860
0.846
TIAToolbox
0.870
0.747
0.750
0.748
0.810
0.790
References
[1] Bilal, Mohsin, et al. “Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study.” The Lancet Digital Health 3.12 (2021): e763-e772.
Methods
Define preprocessing steps.
Attributes
- static preproc(image)[source]#
Define preprocessing steps.
- Parameters:
img (
numpy.ndarray
) – An image of shape HWC.image (ndarray) –
- Returns:
An image of shape HWC.
- Return type:
img (
torch.Tensor
)