ArXiv Preprint
Advances in entity-graph based analysis of histopathology images have brought
in a new paradigm to describe tissue composition, and learn the tissue
structure-to-function relationship. Entity-graphs offer flexible and scalable
representations to characterize tissue organization, while allowing the
incorporation of prior pathological knowledge to further support model
interpretability and explainability. However, entity-graph analysis requires
prerequisites for image-to-graph translation and knowledge of state-of-the-art
machine learning algorithms applied to graph-structured data, which can
potentially hinder their adoption. In this work, we aim to alleviate these
issues by developing HistoCartography, a standardized python API with necessary
preprocessing, machine learning and explainability tools to facilitate
graph-analytics in computational pathology. Further, we have benchmarked the
computational time and performance on multiple datasets across different
imaging types and histopathology tasks to highlight the applicability of the
API for building computational pathology workflows.
Guillaume Jaume, Pushpak Pati, Valentin Anklin, Antonio Foncubierta, Maria Gabrani
2021-07-21
