SlideCheck: Guiding Self-Supervised Pretraining of Pathology Foundation Models via Dataset Distributions
arXiv:2606.07590v1 Announce Type: cross Abstract: Pathology foundation models are pretrained on large streams of WSI-derived patches, while supervision during data construction is often slide-level, sparse, or heterogeneous. This mismatch makes it difficult to understand and control which biological patterns enter the pretraining data. We propose SlideCheck, a lightweight pretraining data guidance tool built on frozen pathology foundation model patch features. Rather than serving as a standalone patch diagnostic model, SlideCheck provides explicit abnormality and malignancy scores for organizi
The proliferation of foundation models across various domains, including pathology, necessitates better control and understanding of their pretraining data to ensure robust and unbiased performance.
Improving the guidance of self-supervised pretraining for pathology foundation models can significantly accelerate medical AI development and improve diagnostic accuracy, directly impacting healthcare outcomes.
The introduction of tools like SlideCheck changes how researchers and developers can curate and understand the biological patterns within large-scale pretraining datasets, moving towards more targeted and efficient model development.
- · Medical AI developers
- · Healthcare providers
- · Patients
- · AI research institutions
- · Disease progression (potentially)
- · Inefficient pathology model development workflows
More accurate and reliable AI models for pathology analysis are developed.
Faster and cheaper drug discovery processes emerge due to improved AI-driven pathology insights.
The democratization of advanced diagnostic capabilities transforms global healthcare access and standards.
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Read at arXiv cs.AI