@conference {cPinaa,
	title = {Self-supervised graph representations of WSIs},
	booktitle = {Geometric Deep Learning in Medical Image Analysis},
	year = {2022},
	month = {2022},
	abstract = {<p><span style="color: $\#$222222; font-family: Arial, Helvetica, sans-serif; font-size: small;">In this manuscript we propose a framework for the analysis of whole slide images (WSI) on the cell entity space with self-supervised deep learning on graphs and explore its representation quality at different levels of application. It consists of a two step process in which the cell level analysis is performed locally, by clusters of nearby cells that can be seen as small regions of the image, in order to learn representations that capture the cell environment and distribution. In a second stage, a WSI graph is generated with these regions as nodes and the representations learned as initial node embeddings. The graph is leveraged for a downstream task, region of interest (ROI) detection addressed as a graph clustering. The representations outperform the evaluation baselines at both levels of application, which has been carried out predicting whether a cell, or region, is tumor or not based on its learned representations with a logistic regressor.</span></p>},
	url = {https://proceedings.mlr.press/v194/pina22a/pina22a.pdf},
	author = {Oscar Pina and Ver{\'o}nica Vilaplana}
}
