融合多模态数据，用改进的BiLSTM-AM-VMD模型提升肝癌早期诊断准确率。
A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture
- 结合临床、生化与影像数据，用双向LSTM+注意力+变分模态分解建模
- 在真实数据集上比传统模型和基线深度学习模型表现更优
- 适合医学人工智能、肝癌早筛方向的研究者参考
本文提出一种新型多模态深度学习框架，整合双向LSTM、多头注意力机制与变分模态分解（BiLSTM-AM-VMD），用于肝癌早期诊断。该方法融合临床特征、生化标志物及影像衍生变量等异构数据，在真实世界数据集上的实验结果表明，其预测性能优于传统机器学习与基线深度学习模型，同时提升了诊断结果的可解释性。
原文摘要 · Abstract (English)
This paper proposes a novel multimodal deep learning framework integrating bidirectional LSTM, multi-head attention mechanism, and variational mode decomposition (BiLSTM-AM-VMD) for early liver cancer diagnosis. Using heterogeneous data that include clinical characteristics, biochemical markers, and imaging-derived variables, our approach improves both prediction accuracy and interpretability. Experimental results on real-world datasets demonstrate superior performance over traditional machine learning and baseline deep learning models.
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