Investigating staining variance effects on deep learning-based semantic segmentation in digital pathology

Amine Marzouki; Zografoula Vagena; Camille Kurtz; Nicolas Loménie

doi:10.1117/12.3006724

3 April 2024 Investigating staining variance effects on deep learning-based semantic segmentation in digital pathology

Amine Marzouki, Zografoula Vagena, Camille Kurtz, Nicolas Loménie

Proceedings Volume 12933, Medical Imaging 2024: Digital and Computational Pathology; 1293312 (2024) https://doi.org/10.1117/12.3006724
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

The adoption of artificial intelligence and digital pathology shows immense promise for transforming healthcare through enhanced efficiency, cost-effectiveness, and patient outcomes. However, real-world clinical deployment of deep learning systems faces major obstacles, including the significant staining variability inherent to histopathology workflows. Differences in protocols, reagents, and scanners cause considerable distribution shifts that undermine model generalization.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Amine Marzouki, Zografoula Vagena, Camille Kurtz, and Nicolas Loménie "Investigating staining variance effects on deep learning-based semantic segmentation in digital pathology", Proc. SPIE 12933, Medical Imaging 2024: Digital and Computational Pathology, 1293312 (3 April 2024); https://doi.org/10.1117/12.3006724

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