MuPD is a pretrained generative foundation model using a diffusion transformer with cross-modal attention that synthesizes histopathology images from text or RNA data and outperforms task-specific models on generation, augmentation, and virtual staining tasks.
Histai: An open-source, large-scale whole slide image dataset for computational pathology
8 Pith papers cite this work. Polarity classification is still indexing.
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A new open pipeline and dataset enable training of a vision-language model for whole-slide pathology VQA that outperforms MedGemma on tissue identification, neoplasm detection, and differential diagnosis.
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.
GRACE, a gastric-specific pathology foundation model trained on multicenter HE-stained slides, outperforms pancancer models on 28 tasks and improves pathologist accuracy, speed, and agreement in a reader study while enabling case triaging.
PulmoFoundation achieves 92.3% average AUC on 32 lung pathology tasks in prospective validation and raises pathologist accuracy from 83.8% to 91.7% in a crossover RCT.
BRAVE foundation model excludes 70-77% of negative breast biopsy and frozen-section cases with NPV above 0.95 while improving balanced accuracy from 88.5% to 95.1% in reader studies and predicting survival outcomes.
A masked-diffusion pretrained convolutional model outperforms ViT pathology foundation models on cell-level dense prediction tasks in histology.
A token-efficient VLM with frozen encoder, two-layer MLP aligner, and LLM decoder generates case-level synoptic pathology reports from multi-WSI inputs using 5x magnification patches and two-stage supervised training.
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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A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation
PulmoFoundation achieves 92.3% average AUC on 32 lung pathology tasks in prospective validation and raises pathologist accuracy from 83.8% to 91.7% in a crossover RCT.