A Novel Cell Map Representation for Weakly Supervised Prediction of ER & PR Status from H&E WSIs

Hammam M. AlGhamdiă, Navid Alemi Koohbanani, Nasir Rajpoot, Shan E. Ahmed Raza
Proceedings of the MICCAI Workshop on Computational Pathology, PMLR 156:10-19, 2021.

Abstract

Digital pathology opens new pathways for computational algorithms to play a significant role in the prognosis, diagnosis, and analysis of cancer. However, handling large whole slide images (WSIs) is a vital challenge that these algorithms encounter. In this paper, we propose a novel technique that creates a compressed representation of histology images. This representation is composed of cellular maps and compresses the WSIs while keeping relevant information at hand including the spatial relationships between cells. The compression technique is used to predict the status of ER & PR expressions from H&E WSIs. Our results show that the proposed compression technique can improve the prediction performance by 11-26%.

Cite this Paper

BibTeX
@InProceedings{pmlr-v156-alghamdia21a, title = {A Novel Cell Map Representation for Weakly Supervised Prediction of ER & PR Status from H&E WSIs}, author = {AlGhamdi\u{a}, Hammam M. and Koohbanani, Navid Alemi and Rajpoot, Nasir and Raza, Shan E. Ahmed}, booktitle = {Proceedings of the MICCAI Workshop on Computational Pathology}, pages = {10--19}, 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/alghamdia21a/alghamdia21a.pdf}, url = {https://proceedings.mlr.press/v156/alghamdia21a.html}, abstract = {Digital pathology opens new pathways for computational algorithms to play a significant role in the prognosis, diagnosis, and analysis of cancer. However, handling large whole slide images (WSIs) is a vital challenge that these algorithms encounter. In this paper, we propose a novel technique that creates a compressed representation of histology images. This representation is composed of cellular maps and compresses the WSIs while keeping relevant information at hand including the spatial relationships between cells. The compression technique is used to predict the status of ER & PR expressions from H&E WSIs. Our results show that the proposed compression technique can improve the prediction performance by 11-26%.} }
Endnote
%0 Conference Paper %T A Novel Cell Map Representation for Weakly Supervised Prediction of ER & PR Status from H&E WSIs %A Hammam M. AlGhamdiă %A Navid Alemi Koohbanani %A Nasir Rajpoot %A Shan E. Ahmed Raza %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-alghamdia21a %I PMLR %P 10--19 %U https://proceedings.mlr.press/v156/alghamdia21a.html %V 156 %X Digital pathology opens new pathways for computational algorithms to play a significant role in the prognosis, diagnosis, and analysis of cancer. However, handling large whole slide images (WSIs) is a vital challenge that these algorithms encounter. In this paper, we propose a novel technique that creates a compressed representation of histology images. This representation is composed of cellular maps and compresses the WSIs while keeping relevant information at hand including the spatial relationships between cells. The compression technique is used to predict the status of ER & PR expressions from H&E WSIs. Our results show that the proposed compression technique can improve the prediction performance by 11-26%.
APA
AlGhamdiă, H.M., Koohbanani, N.A., Rajpoot, N. & Raza, S.E.A.. (2021). A Novel Cell Map Representation for Weakly Supervised Prediction of ER & PR Status from H&E WSIs. Proceedings of the MICCAI Workshop on Computational Pathology, in Proceedings of Machine Learning Research 156:10-19 Available from https://proceedings.mlr.press/v156/alghamdia21a.html.

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