ArticleScientific reports2025
Validation of histopathology foundation models through whole slide image retrieval.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- PathScaleBench: A Multidimensional Benchmark of Cross-Scale Representation Behavior and Magnification-Shift Robustness in Pathology Foundation Models.Journal of imaging informatics in medicine · 2026Article
- Rethinking foundation models in pathology.Nature biomedical engineering · 2026Article
- Towards robust foundation models for digital pathology.Nature communications · 2026Article
- Reliability of foundation models for image retrieval in histopathology.Npj imaging · 2026Article
- Separability of histopathology foundation model embeddings of stimulated Raman histology images for lung carcinoma subtyping.Journal of Cancer · 2026Article
- From microscopy to antimicrobial decisions: a clinically grounded roadmap for critical care infectious diseases.Frontiers in artificial intelligence · 2026Review
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Authors and funding
6 authors.
Funding
Abstract
We evaluated several foundation models in histopathology for image retrieval using a zero-shot approach. These models generated embeddings that were directly employed for retrieval without additional fine-tuning. Our experiments were conducted on diagnostic slides from The Cancer Genome Atlas (TCGA), which covers 23 organs and 117 cancer subtypes. We used Yottixel as the framework for whole-slide image (WSI) retrieval via patch-based embeddings. Retrieval performance was evaluated using macro-averaged F1 scores for top-1, top-3, and top-5 retrievals. The top-5 retrieval F1 scores indicated varying levels of performance: Yottixel-DenseNet (27% ± 13%), Yottixel-UNI (42% ± 14%), Yottixel-Virchow (40% ± 13%), Yottixel-GigaPath (41% ± 13%), and GigaPath WSI (40% ± 14%). These results demonstrate the potential and limitations of foundation models for histopathology image retrieval, underscoring the need for further advancements in embedding and retrieval techniques.
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Registered trials
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