SlideCheck: Guiding Self-Supervised Pretraining of Pathology Foundation Models via Dataset Distributions
SlideCheck is a lightweight tool that uses frozen patch features from pathology foundation models to score abnormality and malignancy in whole-slide images, enabling better control over pretraining data composition. Experiments show that data distributions defined by SlideCheck influence downstream ViT pretraining performance, and curated subsets can match full-data results.
[2606.07590] SlideCheck: Guiding Self-Supervised Pretraining of Pathology Foundation Models via Dataset Distributions
[Submitted on 28 May 2026]
Title:SlideCheck: Guiding Self-Supervised Pretraining of Pathology Foundation Models via Dataset Distributions
View a PDF of the paper titled SlideCheck: Guiding Self-Supervised Pretraining of Pathology Foundation Models via Dataset Distributions, by Mingyi He and 9 other authors
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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 organizing, filtering, and auditing pathology pretraining data. SlideCheck uses a dual-head MLP to separately model broad abnormal morphology and malignant evidence. A regularized feature-space scorer provides a supervised anchor for patch-level evidence estimation, while score-attention agreement combines patch scores with WSI-level MIL attention to mine high-confidence pseudo labels. The same scores are then used to construct broad-positive ViT pretraining subsets, where a patch is selected if either abnormality or malignancy evidence exceeds a threshold. Experiments show that SlideCheck-defined data distributions influence the downstream behavior of self-supervised ViT pretraining, indicating that biological composition is an important controllable factor in pathology foundation model development. Curated subsets can approach full-data performance, suggesting that explicitly scored patch pools may support more efficient and auditable pretraining data construction. These findings position SlideCheck as a data guidance and auditing layer for transforming large, undifferentiated patch pools into controllable and reusable pretraining datasets.
Comments: 9 pages, 2 figures, 4 tables
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.07590 [cs.CV]
(or arXiv:2606.07590v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.07590
arXiv-issued DOI via DataCite
Submission history
From: Xinyi Guo [view email] [v1] Thu, 28 May 2026 13:05:45 UTC (81 KB)
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