Towards Reliable Stain Transfer: An Iterative Data-Model Co-Optimization Framework Based on Multimodal Expert-Guided Assessment
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TL;DR - DMCoStain is an iterative data-model co-optimization framework that generates IHC-stained images from pixel-unaligned H&E data. It aims to improve biomarker staining accuracy, structural consistency, and clinical interpretability while reducing reliance on costly physical IHC staining.
- Uses multimodal expert-guided sample selection powered by an IHC-positive-expression vision-language model that emulates pathologist reasoning.
- Introduces ImmunoInstruction, a 150K-sample visual question-answering dataset for IHC positive-expression assessment.
- Iteratively refines both training data and model capability across heterogeneous tissues and biomarkers.
- Reportedly achieves state-of-the-art accuracy in the evaluated settings; the selection method can also serve as a specialized assessment tool.
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Towards Reliable Stain Transfer: An Iterative Data-Model Co-Optimization Framework Based on Multimodal Expert-Guided Assessment
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Hugging Face upvotes 0
TL;DR - DMCoStain is an iterative data-model co-optimization framework that generates IHC-stained images from pixel-unaligned H&E data. It aims to improve biomarker staining accuracy, structural consistency, and clinical interpretability while reducing reliance on costly physical IHC staining.
- Uses multimodal expert-guided sample selection powered by an IHC-positive-expression vision-language model that emulates pathologist reasoning.
- Introduces ImmunoInstruction, a 150K-sample visual question-answering dataset for IHC positive-expression assessment.
- Iteratively refines both training data and model capability across heterogeneous tissues and biomarkers.
- Reportedly achieves state-of-the-art accuracy in the evaluated settings; the selection method can also serve as a specialized assessment tool.