Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology

Joseph Boyd, Mykola Liashuha, Eric Deutsch, Nikos Paragios, Stergios Christodoulidis, Maria Vakalopoulou

ICCV 2021, workshop on Computational Challenges in Digital Pathology (CDpath)

[arxiv], [code]

Abstract: The examination of histopathology images is considered to be the gold standard for the diagnosis and stratification of cancer patients. A key challenge in the analysis of such images is their size, which can run into the gigapixels and can require tedious screening by clinicians. With the recent advances in computational medicine, automatic tools have been proposed to assist clinicians in their everyday practice. Such tools typically process these large images by slicing them into tiles that can then be encoded and utilized for different clinical models. In this study, we propose a novel generative framework that can learn powerful representations for such tiles by learning to plausibly expand their visual field. In particular, we developed a progressively grown generative model with the objective of visual field expansion. Thus trained, our model learns to generate different tissue types with fine details, while simultaneously learning powerful representations that can be used for different clinical endpoints, all in a self-supervised way. To evaluate the performance of our model, we conducted classification experiments on CAMELYON17 and CRC benchmark datasets, comparing favorably to other self-supervised and pre-trained strategies that are commonly used in digital pathology.

@inproceedings{boyd2021self,
  title={Self-supervised representation learning using visual field expansion on digital pathology},
  author={Boyd, Joseph and Liashuha, Mykola and Deutsch, Eric and Paragios, Nikos and Christodoulidis, Stergios and Vakalopoulou, Maria},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={639--647},
  year={2021}
}