Creating small but meaningful representations of digital pathology images

Corentin Gueréndel, Phil Arnold, Ben Torben-Nielsen
Proceedings of the MICCAI Workshop on Computational Pathology, PMLR 156:206-215, 2021.

Abstract

Representation learning is a popular application of deep learning where an object (e.g., an image) is converted into a lower-dimensional representation that still encodes relevant features of the original object. In digital pathology, however, this is more difficult because whole slide images (WSIs) are tiled before processing because they are too large to process at once. As a result, one WSI can be represented by thousands of representations - one for each tile. Common strategies to aggregate the "tile-level representations" to a "slide-level representation" rely on pooling operators or even attention networks, which all find some weighted average of the tile-level representations. In this work, we propose a novel approach to aggregate tile-level representations into a single slide-level representation. Our method is based on clustering representations from individual tiles that originate from a large pool of WSIs. Each cluster can be seen as encoding a specific feature that might occur in a tile. Then, the final slide-level representation is a function of the proportional cluster membership of all tiles from one WSI. We demonstrate that we can represent WSIs in parsimonious representations and that these aggregated slide-level representations allow for both WSI classification and, reversely, similar image search.

Cite this Paper

BibTeX
@InProceedings{pmlr-v156-guerendel21a, title = {Creating small but meaningful representations of digital pathology images}, author = {Guer\'endel, Corentin and Arnold, Phil and Torben-Nielsen, Ben}, booktitle = {Proceedings of the MICCAI Workshop on Computational Pathology}, pages = {206--215}, 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/guerendel21a/guerendel21a.pdf}, url = {https://proceedings.mlr.press/v156/guerendel21a.html}, abstract = {Representation learning is a popular application of deep learning where an object (e.g., an image) is converted into a lower-dimensional representation that still encodes relevant features of the original object. In digital pathology, however, this is more difficult because whole slide images (WSIs) are tiled before processing because they are too large to process at once. As a result, one WSI can be represented by thousands of representations - one for each tile. Common strategies to aggregate the "tile-level representations" to a "slide-level representation" rely on pooling operators or even attention networks, which all find some weighted average of the tile-level representations. In this work, we propose a novel approach to aggregate tile-level representations into a single slide-level representation. Our method is based on clustering representations from individual tiles that originate from a large pool of WSIs. Each cluster can be seen as encoding a specific feature that might occur in a tile. Then, the final slide-level representation is a function of the proportional cluster membership of all tiles from one WSI. We demonstrate that we can represent WSIs in parsimonious representations and that these aggregated slide-level representations allow for both WSI classification and, reversely, similar image search. } }
Endnote
%0 Conference Paper %T Creating small but meaningful representations of digital pathology images %A Corentin Gueréndel %A Phil Arnold %A Ben Torben-Nielsen %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-guerendel21a %I PMLR %P 206--215 %U https://proceedings.mlr.press/v156/guerendel21a.html %V 156 %X Representation learning is a popular application of deep learning where an object (e.g., an image) is converted into a lower-dimensional representation that still encodes relevant features of the original object. In digital pathology, however, this is more difficult because whole slide images (WSIs) are tiled before processing because they are too large to process at once. As a result, one WSI can be represented by thousands of representations - one for each tile. Common strategies to aggregate the "tile-level representations" to a "slide-level representation" rely on pooling operators or even attention networks, which all find some weighted average of the tile-level representations. In this work, we propose a novel approach to aggregate tile-level representations into a single slide-level representation. Our method is based on clustering representations from individual tiles that originate from a large pool of WSIs. Each cluster can be seen as encoding a specific feature that might occur in a tile. Then, the final slide-level representation is a function of the proportional cluster membership of all tiles from one WSI. We demonstrate that we can represent WSIs in parsimonious representations and that these aggregated slide-level representations allow for both WSI classification and, reversely, similar image search.
APA
Gueréndel, C., Arnold, P. & Torben-Nielsen, B.. (2021). Creating small but meaningful representations of digital pathology images. Proceedings of the MICCAI Workshop on Computational Pathology, in Proceedings of Machine Learning Research 156:206-215 Available from https://proceedings.mlr.press/v156/guerendel21a.html.

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