Disentangled Autoencoder for Cross-Stain Feature Extraction in Pathology Image Analysis
| Authors | |
|---|---|
| Year of publication | 2020 |
| Type | Article in Periodical |
| Magazine / Source | Applied Sciences |
| MU Faculty or unit | |
| Citation | |
| Web | https://doi.org/10.3390/app10186427 |
| Doi | http://dx.doi.org/10.3390/app10186427 |
| Keywords | digital pathology; image registration; deep learning; disentangled autoencoder |
| Attached files | |
| Description | A novel deep autoencoder architecture is proposed for the analysis of histopathology images. Its purpose is to produce a disentangled latent representation in which the structure and colour information are confined to different subspaces so that stain-independent models may be learned. For this, we introduce two constraints on the representation which are implemented as a classifier and an adversarial discriminator. We show how they can be used for learning a latent representation across haematoxylin-eosin and a number of immune stains. Finally, we demonstrate the utility of the proposed representation in the context of matching image patches for registration applications and for learning a bag of visual words for whole slide image summarization. |
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