histocartography.preprocessing package

Submodules:

• histocartography.preprocessing.assignment_matrix module
• histocartography.preprocessing.feature_extraction module
• histocartography.preprocessing.graph_builders module
• histocartography.preprocessing.io module
• histocartography.preprocessing.nuclei_concept_extraction module
• histocartography.preprocessing.nuclei_extraction module
• histocartography.preprocessing.stain_normalizers module
• histocartography.preprocessing.stats module
• histocartography.preprocessing.superpixel module
• histocartography.preprocessing.tissue_mask module
• histocartography.preprocessing.utils module

Summary

__all__ Classes:

AnnotationPostProcessor	Base pipelines step
AssignmnentMatrixBuilder	Assigning low-level instances to high-level instances using instance maps.
AugmentedDeepFeatureExtractor	Helper class to extract deep features from instance maps with different augmentations
ColorMergedSuperpixelExtractor	Helper class to extract superpixels from images
DGLGraphLoader	Base pipelines step
DeepFeatureExtractor	Helper class to extract deep features from instance maps
GaussianTissueMask	Helper class to extract tissue mask from images
GraphDiameter	Base pipelines step
GridAugmentedDeepFeatureExtractor	Base class for feature extraction
GridDeepFeatureExtractor	Base class for feature extraction
H5Loader	Base pipelines step
HandcraftedFeatureExtractor	Helper class to extract handcrafted features from instance maps
ImageLoader	Base pipelines step
KNNGraphBuilder	k-Nearest Neighbors Graph class for graph building.
MacenkoStainNormalizer	Stain normalization based on the method of: M.
NucleiConceptExtractor	Class for Nuclei concept extraction.
NucleiExtractor	Nuclei extraction
RAGGraphBuilder	Super-pixel Graphs class for graph building.
SLICSuperpixelExtractor	Use the SLIC algorithm to extract superpixels.
SuperpixelCounter	Base pipelines step
VahadaneStainNormalizer	Stain normalization inspired by method of: A.

Reference

If you use histocartography in your projects, please cite the following:

@inproceedings{pati2021,
    title = {Hierarchical Graph Representations for Digital Pathology},
    author = {Pushpak Pati, Guillaume Jaume, Antonio Foncubierta, Florinda Feroce, Anna Maria Anniciello, Giosuè Scognamiglio, Nadia Brancati, Maryse Fiche, Estelle Dubruc, Daniel Riccio, Maurizio Di Bonito, Giuseppe De Pietro, Gerardo Botti, Jean-Philippe Thiran, Maria Frucci, Orcun Goksel, Maria Gabrani},
    booktitle = {https://arxiv.org/pdf/2102.11057},
    year = {2021}
}