Deep Learning for interpretable end-to-end survival (E-ESurv) prediction in gastrointestinal cancer histopathology

Narmin Ghaffari Laleh, Amelie Echle, Hannah Sophie Muti, Katherine Jane Hewitt, Schulz Volkmar, Jakob Nikolas Kather
Proceedings of the MICCAI Workshop on Computational Pathology, PMLR 156:81-93, 2021.

Abstract

This paper demonstrates and validates EE-Surv, a powerful yet algorithmically simple method to predict survival directly from whole slide images which we validate in colorectal and gastric cancer, two clinically relevant and markedly different tumor types.

Cite this Paper

BibTeX
@InProceedings{pmlr-v156-ghaffari-laleh21a, title = {Deep Learning for interpretable end-to-end survival (E-ESurv) prediction in gastrointestinal cancer histopathology}, author = {Ghaffari Laleh, Narmin and Echle, Amelie and Muti, Hannah Sophie and Hewitt, Katherine Jane and Volkmar, Schulz and Kather, Jakob Nikolas}, booktitle = {Proceedings of the MICCAI Workshop on Computational Pathology}, pages = {81--93}, 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/ghaffari-laleh21a/ghaffari-laleh21a.pdf}, url = {https://proceedings.mlr.press/v156/ghaffari-laleh21a.html}, abstract = {This paper demonstrates and validates EE-Surv, a powerful yet algorithmically simple method to predict survival directly from whole slide images which we validate in colorectal and gastric cancer, two clinically relevant and markedly different tumor types.} }
Endnote
%0 Conference Paper %T Deep Learning for interpretable end-to-end survival (E-ESurv) prediction in gastrointestinal cancer histopathology %A Narmin Ghaffari Laleh %A Amelie Echle %A Hannah Sophie Muti %A Katherine Jane Hewitt %A Schulz Volkmar %A Jakob Nikolas Kather %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-ghaffari-laleh21a %I PMLR %P 81--93 %U https://proceedings.mlr.press/v156/ghaffari-laleh21a.html %V 156 %X This paper demonstrates and validates EE-Surv, a powerful yet algorithmically simple method to predict survival directly from whole slide images which we validate in colorectal and gastric cancer, two clinically relevant and markedly different tumor types.
APA
Ghaffari Laleh, N., Echle, A., Muti, H.S., Hewitt, K.J., Volkmar, S. & Kather, J.N.. (2021). Deep Learning for interpretable end-to-end survival (E-ESurv) prediction in gastrointestinal cancer histopathology. Proceedings of the MICCAI Workshop on Computational Pathology, in Proceedings of Machine Learning Research 156:81-93 Available from https://proceedings.mlr.press/v156/ghaffari-laleh21a.html.

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