PathLab is an agentic framework that translates natural-language objectives into validated computational pathology workflows, achieving non-inferior performance on 12 datasets across four task families while enabling non-programmers to conduct studies.
A survey on com- putational pathology foundation models: Datasets, adaptation strategies, and evaluation tasks
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SlideCheck uses a dual-head MLP on frozen features plus MIL attention to score patches and filter pretraining subsets that approach full-data performance in self-supervised pathology ViT models.
H-optimus-1 achieves the strongest externally validated survival prediction from histopathology images, with second-generation PFMs outperforming first-generation counterparts and a compact distilled model offering efficiency gains.
Foundation models stumble in pathology due to conceptual mismatches with biological tissue, requiring explicitly designed models rather than adaptations of natural-image methods.
citing papers explorer
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Democratizing and accelerating AI-driven pathology research through agentic intelligence
PathLab is an agentic framework that translates natural-language objectives into validated computational pathology workflows, achieving non-inferior performance on 12 datasets across four task families while enabling non-programmers to conduct studies.
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SlideCheck: Guiding Self-Supervised Pretraining of Pathology Foundation Models via Dataset Distributions
SlideCheck uses a dual-head MLP on frozen features plus MIL attention to score patches and filter pretraining subsets that approach full-data performance in self-supervised pathology ViT models.
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Benchmarking Pathology Foundation Models for Breast Cancer Survival Prediction
H-optimus-1 achieves the strongest externally validated survival prediction from histopathology images, with second-generation PFMs outperforming first-generation counterparts and a compact distilled model offering efficiency gains.
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Beyond the Failures: Rethinking Foundation Models in Pathology
Foundation models stumble in pathology due to conceptual mismatches with biological tissue, requiring explicitly designed models rather than adaptations of natural-image methods.