A stepwise clinically-guided multimodal attention model for pCR prediction from breast MRI improves sensitivity and cross-institutional generalization over non-guided baselines.
Clinical cancer research26(12), 2838–2848 (2020)
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ClinRAG-GRAPH fuses graph convolutional networks, dual-branch domain-adversarial learning, and LLM-driven subgraph retrieval to predict pre-treatment pathological complete response from DCE-MRI and clinical data, reporting AUC 0.815 internal and 0.774/0.712 on two external sets.
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Multimodal Stepwise Clinically-Guided Attention Learning for Pathological Complete Response Prediction in Breast Cancer
A stepwise clinically-guided multimodal attention model for pCR prediction from breast MRI improves sensitivity and cross-institutional generalization over non-guided baselines.
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ClinRAG-GRAPH: Clinical-prior Retrieval-Augmented Graph Model with Domain Adversarial Learning for Breast pCR Prediction
ClinRAG-GRAPH fuses graph convolutional networks, dual-branch domain-adversarial learning, and LLM-driven subgraph retrieval to predict pre-treatment pathological complete response from DCE-MRI and clinical data, reporting AUC 0.815 internal and 0.774/0.712 on two external sets.