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

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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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Thursday 7th July
Poster Session 2.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)
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