HistoCartography: A Toolkit for Graph Analytics in Digital Pathology

Guillaume Jaume, Pushpak Pati, Valentin Anklin, Antonio Foncubierta, Maria Gabrani
Proceedings of the MICCAI Workshop on Computational Pathology, PMLR 156:117-128, 2021.

Abstract

Advances in entity-graph 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 explainability. However, their analysis requires prerequisites for image-to-graph translation and knowledge of state-of-the-art 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. HistoCartography is available at https://github.com/histocartography/histocartography.

Cite this Paper

BibTeX
@InProceedings{pmlr-v156-jaume21a, title = {HistoCartography: A Toolkit for Graph Analytics in Digital Pathology}, author = {Jaume, Guillaume and Pati, Pushpak and Anklin, Valentin and Foncubierta, Antonio and Gabrani, Maria}, booktitle = {Proceedings of the MICCAI Workshop on Computational Pathology}, pages = {117--128}, year = {2021}, editor = {Atzori, Manfredo and Burlutskiy, Nikolay and Ciompi, Francesco and Li, Zhang and Minhas, Fayyaz and Müller, Henning and Peng, Tingying and Rajpoot, Nasir and Torben-Nielsen, Ben and van der Laak, Jeroen and Veta, Mitko and Yuan, Yinyin and Zlobec, Inti}, volume = {156}, series = {Proceedings of Machine Learning Research}, month = {27 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v156/jaume21a/jaume21a.pdf}, url = {https://proceedings.mlr.press/v156/jaume21a.html}, abstract = {Advances in entity-graph 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 explainability. However, their analysis requires prerequisites for image-to-graph translation and knowledge of state-of-the-art 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. HistoCartography is available at https://github.com/histocartography/histocartography.} }
Endnote
%0 Conference Paper %T HistoCartography: A Toolkit for Graph Analytics in Digital Pathology %A Guillaume Jaume %A Pushpak Pati %A Valentin Anklin %A Antonio Foncubierta %A Maria Gabrani %B Proceedings of the MICCAI Workshop on Computational Pathology %C Proceedings of Machine Learning Research %D 2021 %E Manfredo Atzori %E Nikolay Burlutskiy %E Francesco Ciompi %E Zhang Li %E Fayyaz Minhas %E Henning Müller %E Tingying Peng %E Nasir Rajpoot %E Ben Torben-Nielsen %E Jeroen van der Laak %E Mitko Veta %E Yinyin Yuan %E Inti Zlobec %F pmlr-v156-jaume21a %I PMLR %P 117--128 %U https://proceedings.mlr.press/v156/jaume21a.html %V 156 %X Advances in entity-graph 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 explainability. However, their analysis requires prerequisites for image-to-graph translation and knowledge of state-of-the-art 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. HistoCartography is available at https://github.com/histocartography/histocartography.
APA
Jaume, G., Pati, P., Anklin, V., Foncubierta, A. & Gabrani, M.. (2021). HistoCartography: A Toolkit for Graph Analytics in Digital Pathology. Proceedings of the MICCAI Workshop on Computational Pathology, in Proceedings of Machine Learning Research 156:117-128 Available from https://proceedings.mlr.press/v156/jaume21a.html.

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