Sparse capsule networks for informative representation learning in digital pathology

Matthew McNeil; Cem Anil; Anne Martel

doi:10.1117/12.2611584

4 April 2022 Sparse capsule networks for informative representation learning in digital pathology

Matthew McNeil, Cem Anil, Anne Martel

Proceedings Volume 12039, Medical Imaging 2022: Digital and Computational Pathology; 120391E (2022) https://doi.org/10.1117/12.2611584
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Digital pathology involves the digitization of high quality tissue biopsies on microscope slides to be used by physicians for patient diagnosis and prognosis. These slides have become exciting avenues for deep learning applications to improve care. Despite this, labels are difficult to produce and thus remain rare. In this work, we create a sparse capsule network with a spatial broadcast decoder to perform representation learning on segmented nuclei patches extracted from the BreastPathQ dataset. This was able to produce disentangled latent space for categories such as rotations, and logistic regression classifiers trained on the latent space performed well.

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Citation Download Citation

Matthew McNeil, Cem Anil, and Anne Martel "Sparse capsule networks for informative representation learning in digital pathology", Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 120391E (4 April 2022); https://doi.org/10.1117/12.2611584

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