融合影像与临床数据，用医生知识引导模型预测肺癌治疗反应。
Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer
- 多模态数据中间融合，实现影像与临床信息高效交互。
- 模型准确率提升，能逐步聚焦到具体病灶区域。
- 适合临床医生参与的可解释性建模，提升决策可信度。
本研究提出一种结合多模态深度学习与内在可解释人工智能的新方法，用于预测接受新辅助治疗的非小细胞肺癌患者的病理反应。针对现有影像组学和单模态深度学习方法的局限性，我们引入一种中间融合策略，整合影像与临床数据，实现模态间的高效交互。所提出的多模态医生在环（Multimodal Doctor-in-the-Loop）方法通过将临床医生的专业知识嵌入训练过程，引导模型注意力从整体肺部逐渐聚焦至特定病灶，显著提升预测准确性与可解释性，为临床应用提供最优数据融合思路。
原文摘要 · Abstract (English)
This study proposes a novel approach combining Multimodal Deep Learning with intrinsic eXplainable Artificial Intelligence techniques to predict pathological response in non-small cell lung cancer patients undergoing neoadjuvant therapy. Due to the limitations of existing radiomics and unimodal deep learning approaches, we introduce an intermediate fusion strategy that integrates imaging and clinical data, enabling efficient interaction between data modalities. The proposed Multimodal Doctor-in-the-Loop method further enhances clinical relevance by embedding clinicians' domain knowledge directly into the training process, guiding the model's focus gradually from broader lung regions to specific lesions. Results demonstrate improved predictive accuracy and explainability, providing insights into optimal data integration strategies for clinical applications.
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