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
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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.
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.
citing papers explorer
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A Generative Foundation Model for Multimodal Histopathology
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.
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Democratising Pathology Co-Pilots: An Open Pipeline and Dataset for Whole-Slide Vision-Language Modelling
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.
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A Breast Vision Pathology Foundation Model for Real-world Clinical Utility
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.
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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.