融合影像与基因数据，提升肺癌分类准确率至94.04%
Multi-modal Medical Image Fusion For Non-Small Cell Lung Cancer Classification
- 整合CT、PET、临床与基因数据，用MedClip和BEiT提取特征
- 多模态模型准确率达94.04%，各项指标均优于现有方法
- 适合临床辅助诊断与精准医疗研究者参考
非小细胞肺癌（NSCLC）是全球癌症死亡主因之一，其早期检测与亚型分类至关重要且复杂。本文提出一种创新的多模态数据融合方法，结合医学影像（CT与PET）、临床健康记录及基因组数据。通过先进机器学习模型（如MedClip和BEiT）进行图像特征提取，构建多模态分类器。实验结果表明，该方法在检测与分类精度上显著超越现有方法，关键性能指标全面提升：准确率、精确率、召回率与F1分数均实现明显增长。其中最优模型准确率达到94.04%。本研究为计算肿瘤学提供新范式，有望推动肺癌早期诊断与精准治疗，改善患者预后。
原文摘要 · Abstract (English)
The early detection and nuanced subtype classification of non-small cell lung cancer (NSCLC), a predominant cause of cancer mortality worldwide, is a critical and complex issue. In this paper, we introduce an innovative integration of multi-modal data, synthesizing fused medical imaging (CT and PET scans) with clinical health records and genomic data. This unique fusion methodology leverages advanced machine learning models, notably MedClip and BEiT, for sophisticated image feature extraction, setting a new standard in computational oncology. Our research surpasses existing approaches, as evidenced by a substantial enhancement in NSCLC detection and classification precision. The results showcase notable improvements across key performance metrics, including accuracy, precision, recall, and F1-score. Specifically, our leading multi-modal classifier model records an impressive accuracy of 94.04%. We believe that our approach has the potential to transform NSCLC diagnostics, facilitating earlier detection and more effective treatment planning and, ultimately, leading to superior patient outcomes in lung cancer care.
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