SpaPath-Bench evaluates spatial representation in 19 pathology foundation models via spatial domain identification on 42 paired WSI-ST slides using three agreement criteria across 83K runs.
Hibou: A family of foundational vision transformers for pathology
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CellDX AI Autopilot lets users train pathology classifiers via AI agent skills on a large pre-extracted whole-slide image dataset with automated hyperparameter tuning that claims over 30x cost reduction.
Novel robustness losses added during downstream training on foundation-model features from pathology slides improve both robustness to technical variation and classification accuracy.
DICE ensembles frozen pathology foundation models, aligns them with deep mutual learning to make disagreement a reliable uncertainty proxy, and shows consensus-based localization on WSI tasks.
GLMP generates robust pathology embeddings by routing histology images through an intermediate textual representation produced by general-purpose MLLMs to mitigate batch effects.
A masked-diffusion pretrained convolutional model outperforms ViT pathology foundation models on cell-level dense prediction tasks in histology.
Pathology foundation models deliver strong in-distribution prostate cancer grading performance but exhibit large drops under cross-site image appearance shifts while remaining relatively robust to label distribution shifts.
CellPrior-Net integrates hematoxylin channel prior into a lightweight CNN for nuclei detection and classification in H&E WSIs, claiming comparable accuracy to SOTA with significantly reduced inference time across 10.4M nuclei from diverse datasets.
citing papers explorer
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Benchmarking Pathology Foundation Models for Spatial Domain Understanding
SpaPath-Bench evaluates spatial representation in 19 pathology foundation models via spatial domain identification on 42 paired WSI-ST slides using three agreement criteria across 83K runs.
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CellDX AI Autopilot: Agent-Guided Training and Deployment of Pathology Classifiers
CellDX AI Autopilot lets users train pathology classifiers via AI agent skills on a large pre-extracted whole-slide image dataset with automated hyperparameter tuning that claims over 30x cost reduction.
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Enabling clinical use of foundation models for computational pathology
Novel robustness losses added during downstream training on foundation-model features from pathology slides improve both robustness to technical variation and classification accuracy.
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Uncertainty Estimation in Pathology Foundation Models via Deep Mutual Learning
DICE ensembles frozen pathology foundation models, aligns them with deep mutual learning to make disagreement a reliable uncertainty proxy, and shows consensus-based localization on WSI tasks.
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Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation
GLMP generates robust pathology embeddings by routing histology images through an intermediate textual representation produced by general-purpose MLLMs to mitigate batch effects.
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Beyond ViT Tokens: Masked-Diffusion Pretrained Convolutional Pathology Foundation Model for Cell-Level Dense Prediction
A masked-diffusion pretrained convolutional model outperforms ViT pathology foundation models on cell-level dense prediction tasks in histology.
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Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts
Pathology foundation models deliver strong in-distribution prostate cancer grading performance but exhibit large drops under cross-site image appearance shifts while remaining relatively robust to label distribution shifts.
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CellPrior-Net: Prior-Guided Nuclei Detection and Classification for H&E Whole-Slide Images
CellPrior-Net integrates hematoxylin channel prior into a lightweight CNN for nuclei detection and classification in H&E WSIs, claiming comparable accuracy to SOTA with significantly reduced inference time across 10.4M nuclei from diverse datasets.