针对鼻咽癌患者数据缺失问题，提出自适应网络提升生存预测准确性。
IMAN: An Adaptive Network for Robust NPC Mortality Prediction with Missing Modalities
- 设计自适应融合模块，动态补偿缺失模态信息
- 在多模态数据缺失场景下，死亡预测准确率显著优于传统方法
- 适用于临床中影像与报告不全的鼻咽癌患者风险评估
鼻咽癌是一种复杂的恶性肿瘤，尤其在晚期阶段治疗难度大，精准预测死亡风险对优化治疗策略和改善患者预后至关重要。然而，该预测过程常因鼻咽癌相关数据的高维性和异质性，以及多模态数据普遍缺失（如影像缺失或诊断报告不全）而受阻。传统机器学习方法在面对不完整数据时性能显著下降，难以有效处理高维特征与跨模态复杂关联。即使先进的多模态学习技术（如Transformer）也因缺乏专门的自适应融合机制，在缺失模态情况下仍难以保持鲁棒性能。为此，本文提出IMAN：一种针对缺失模态的鼻咽癌死亡预测自适应网络，通过动态整合与对齐不同数据类型，捕捉复杂数据中的细微模式与上下文关系，实现更可靠的生存预测。
原文摘要 · Abstract (English)
Accurate prediction of mortality in nasopharyngeal carcinoma (NPC), a complex malignancy particularly challenging in advanced stages, is crucial for optimizing treatment strategies and improving patient outcomes. However, this predictive process is often compromised by the high-dimensional and heterogeneous nature of NPC-related data, coupled with the pervasive issue of incomplete multi-modal data, manifesting as missing radiological images or incomplete diagnostic reports. Traditional machine learning approaches suffer significant performance degradation when faced with such incomplete data, as they fail to effectively handle the high-dimensionality and intricate correlations across modalities. Even advanced multi-modal learning techniques like Transformers struggle to maintain robust performance in the presence of missing modalities, as they lack specialized mechanisms to adaptively integrate and align the diverse data types, while also capturing nuanced patterns and contextual relationships within the complex NPC data. To address these problem, we introduce IMAN: an adaptive network for robust NPC mortality prediction with missing modalities.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。