用于从CT和PET分类非小细胞肺癌亚型的多模态学习多阶段中间融合
Multi-stage intermediate fusion for multimodal learning to classify non-small cell lung cancer subtypes from CT and PET
AI总结：
本研究提出一种多阶段中间融合方法，通过体素级融合整合CT和PET图像以分类非小细胞肺癌亚型，其准确率和AUC分别达0.724和0.681，优于单模态及现有融合方法，有效提升非侵入性诊断准确性。
摘要：
在精准医学时代，准确分类非小细胞肺癌（NSCLC）的组织学亚型至关重要，然而当前的侵入性技术并非总是可行，且可能导致临床并发症。本研究提出了一种多阶段中间融合方法，从CT和PET图像中对NSCLC亚型进行分类。我们的方法在特征提取的不同阶段整合这两种模态，使用体素级融合来利用不同抽象层级的互补信息，同时保留空间相关性。我们将该方法与仅使用CT或PET图像的单模态方法进行比较，以证明模态融合的益处，并进一步将其与早期和晚期融合技术进行基准测试，以突出在特征提取期间中间融合的优势。此外，我们将我们的模型与使用PET/CT图像进行组织学亚型分类的唯一现有中间融合方法进行比较。我们的结果表明，所提出的方法在关键指标上优于所有替代方案，其准确率和AUC分别等于0.724和0.681。这种非侵入性方法有潜力显著提高诊断准确性，促进更明智的治疗决策，并推进肺癌管理中的个性化护理。
英文摘要：
Accurate classification of histological subtypes of non-small cell lung cancer (NSCLC) is essential in the era of precision medicine, yet current invasive techniques are not always feasible and may lead to clinical complications. This study presents a multi-stage intermediate fusion approach to classify NSCLC subtypes from CT and PET images. Our method integrates the two modalities at different stages of feature extraction, using voxel-wise fusion to exploit complementary information across varying abstraction levels while preserving spatial correlations. We compare our method against unimodal approaches using only CT or PET images to demonstrate the benefits of modality fusion, and further benchmark it against early and late fusion techniques to highlight the advantages of intermediate fusion during feature extraction. Additionally, we compare our model with the only existing intermediate fusion method for histological subtype classification using PET/CT images. Our results demonstrate that the proposed method outperforms all alternatives across key metrics, with an accuracy and AUC equal to 0.724 and 0.681, respectively. This non-invasive approach has the potential to significantly improve diagnostic accuracy, facilitate more informed treatment decisions, and advance personalized care in lung cancer management.
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。
