HistoCartography: A Toolkit for Graph Analytics in Digital Pathology
This toolkit addresses barriers for researchers in computational pathology by providing a standardized API, though it is incremental as it builds on existing graph-based methods.
The authors tackled the challenge of adopting entity-graph analysis in digital pathology by developing HistoCartography, a toolkit that standardizes preprocessing, machine learning, and explainability tools, resulting in benchmarked performance across multiple datasets and imaging types.
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.