The study questions the traditional belief in stain normalization's necessity and explores the impact of various augmentations on downstream performance. It highlights Lunit-DINO and CTransPath as superior feature extractors for weakly supervised pathology tasks, showcasing their robustness to variations in stain and augmentations.
The research evaluates over 8,000 models across nine tasks, five datasets, three architectures, and multiple preprocessing setups. It finds that omitting stain normalization and image augmentations does not compromise classification performance while saving memory and compute resources. The study emphasizes clinical relevance by focusing on slide-level biomarker prediction tasks in a weakly supervised setting with external validation cohorts.
Key findings include the insignificance of stain normalization and augmentation effects on downstream performance across different feature extractors. The analysis showcases how top-performing SSL models trained on pathology data outperform ImageNet baselines, suggesting tailored SSL methods are crucial for effective feature extraction in pathology.
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by Geor... om arxiv.org 03-06-2024
https://arxiv.org/pdf/2311.11772.pdfDiepere vragen