A Semi-Supervised Deep Learning Approach for Multi-Stain Foreground Segmentation in Digital Pathology

Agathe de Vulpian, Valentina di Proietto, Gauthier Roy, Saima Ben Hadj, Rutger RH Fick

Keywords: digital pathology, foreground segmentation, semi-supervised learning, GANs

TL;DR: A semi-supervised training approach to train cross-stain foreground segmentation models in digital pathology from single-stain annotation

Abstract: The analysis of whole computational pathology slides can often be accelerated by excluding background areas from the analysis. Deep learning has proven to be superior to signal processing techniques to robustly recover the foreground in HE Images. However, naively generalizing this technique to the wide variability of histological stains used in practice would require annotations in all stain domains. To avoid this, we propose a method which leverages tissue annotation from a single stain to perform foreground segmentation in slides with other non-annotated stains.

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Paper Type: novel methodological ideas without extensive validation

Primary Subject Area: Segmentation

Secondary Subject Area: Transfer Learning and Domain Adaptation

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