通过多模态表示学习进行肺癌生存预测
Survival Prediction in Lung Cancer through Multi-Modal Representation Learning
AI总结：
本文提出一种融合CT、PET和基因组数据的多模态表示学习方法，通过自监督学习和跨患者模块对齐相似患者嵌入，提升非小细胞肺癌生存预测性能。
摘要：
生存预测是与癌症诊断和治疗规划相关的一项关键任务。本文提出了一种新的生存预测方法，利用来自CT和PET扫描的综合信息以及相关的基因组数据。当前的方法要么依赖单一模态，要么整合多种模态进行预测，但没有充分解决患者之间或模态之间的关联。我们的目标是通过整合多模态影像数据与遗传信息，同时考虑患者之间和模态之间的关联，开发一个稳健的生存结局预测模型。我们通过自监督模块学习每种模态的表示，并利用患者之间的语义相似性来确保嵌入紧密对齐。然而，仅针对全局相关性进行优化是不够的，因为许多共享相似高层语义（例如肿瘤类型）的配对在嵌入空间中会被无意推开。为了解决这个问题，我们使用了一个跨患者模块（CPM），旨在利用受试者间的对应关系。CPM模块的目标是将具有相似疾病特征的患者嵌入拉近。我们在非小细胞肺癌（NSCLC）患者数据集上的实验评估证明了我们的方法在预测生存结局方面的有效性，优于最先进的方法。
英文摘要：
Survival prediction is a crucial task associated with cancer diagnosis and treatment planning. This paper presents a novel approach to survival prediction by harnessing comprehensive information from CT and PET scans, along with associated Genomic data. Current methods rely on either a single modality or the integration of multiple modalities for prediction without adequately addressing associations across patients or modalities. We aim to develop a robust predictive model for survival outcomes by integrating multi-modal imaging data with genetic information while accounting for associations across patients and modalities. We learn representations for each modality via a self-supervised module and harness the semantic similarities across the patients to ensure the embeddings are aligned closely. However, optimizing solely for global relevance is inadequate, as many pairs sharing similar high-level semantics, such as tumor type, are inadvertently pushed apart in the embedding space. To address this issue, we use a cross-patient module (CPM) designed to harness inter-subject correspondences. The CPM module aims to bring together embeddings from patients with similar disease characteristics. Our experimental evaluation of the dataset of Non-Small Cell Lung Cancer (NSCLC) patients demonstrates the effectiveness of our approach in predicting survival outcomes, outperforming state-of-the-art methods.
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。
