面向非小细胞肺癌分类的多模态医学图像融合
Multi-modal Medical Image Fusion For Non-Small Cell Lung Cancer Classification
AI总结：
针对非小细胞肺癌早期检测与亚型分类难题，提出融合CT/PET影像、临床记录与基因组数据的多模态方法，借助MedClip、BEiT提取特征，准确率达94.04%，有望提升肺癌诊疗效果。
摘要：
非小细胞肺癌（NSCLC）是全球癌症死亡的主要原因，其早期检测与精细亚型分类是一个关键且复杂的问题。本文提出了一种创新的多模态数据整合方法，将融合后的医学影像（CT和PET扫描）与临床健康记录、基因组数据相结合。这种独特的融合方法利用先进的机器学习模型，尤其是MedClip和BEiT，来进行复杂的图像特征提取，为计算肿瘤学树立了新的标准。我们的研究优于现有方法，这一点可从NSCLC检测与分类精度的大幅提升中得到证明。结果显示，该方法在准确率、精确率、召回率和F1值等关键性能指标上均有显著提升。具体而言，我们性能最优的多模态分类器模型取得了94.04%的出色准确率。我们认为，该方法有望革新NSCLC诊断，推动更早的疾病检测与更有效的治疗方案制定，最终提升肺癌诊疗中的患者预后水平。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
