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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DaX is a pathology vision foundation model that extends DINOv3 with continuous magnification training and cross-scale consistency, achieving top average performance on a benchmark of 161 tasks from 44 datasets covering 28k patients.
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.
MIDOG 2025 challenge shows top mitosis detection F1 of 0.740 and atypical figure balanced accuracy of 0.908 across diverse tumors, with clear drops in challenging regions and tumor-type variation.
citing papers explorer
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DaX: Learning General Pathology Representations Across Scales
DaX is a pathology vision foundation model that extends DINOv3 with continuous magnification training and cross-scale consistency, achieving top average performance on a benchmark of 161 tasks from 44 datasets covering 28k patients.
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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.