Relation-Aware Shared Representation Learning for Cancer Prognosis Analysis With Auxiliary Clinical Variables and Incomplete Multi-Modality Data
Relation-Aware Shared Representation Learning for Cancer Prognosis Analysis With Auxiliary Clinical Variables and Incomplete Multi-Modality Data
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DOI：
10.1109/tmi.2021.3108802
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发表时间：
2022-01-01
影响因子：
10.6
通讯作者：
Zhang，Yu
中科院分区：
文献类型：
--
作者：
Ning，Zhenyuan;Du，Denghui;Zhang，Yu
多模态数据中包含的互补表型信息的综合分析（例如，组织病理学图像和基因组数据）已经推进了癌症的预后评估。然而，基于多模态的预后分析面临两个挑战：（1）如何探索不同模态数据中固有的潜在关系，以学习紧凑且有区分性的多模态表示;（2）如何充分考虑不完整的多模态数据来构建准确且鲁棒的预后模型，因为大量完整的多模态数据并不总是可用的。此外，许多现有的基于多模态的预后方法通常忽略相关的临床变量（例如，等级和阶段），然而，其可以提供补充信息以促进模型的性能。本文提出了一种基于关系感知的共享表示学习方法，该方法充分利用了临床信息和不完整的多模态数据，用于癌症的预后分析。该方法通过对偶映射学习为预测模型量身定制的多模态共享空间。在共享空间内，它配备了关系正则化器来探索潜在的关系（即，特征-标签和特征-特征关系），用于诱导区别性表示，并同时获得额外的稀疏性以减轻过拟合。此外，它回归并结合了多个辅助临床属性与动态系数，以改善性能。此外，在训练阶段，部分映射策略被用来扩展和训练一个更可靠的模型不完整的多模态数据。我们已经评估了我们的方法来自癌症基因组图谱（TCGA）项目的三个公共数据集，实验结果表明，所提出的方法的上级性能。
The integrative analysis of complementary phenotype information contained in multi-modality data (e.g., histopathological images and genomic data) has advanced the prognostic evaluation of cancers. However, multi-modality based prognosis analysis confronts two challenges: (1) how to explore underlying relations inherent in different modalities data for learning compact and discriminative multi-modality representations; (2) how to take full consideration of incomplete multi-modality data for constructing accurate and robust prognostic model, since a host of complete multi-modality data are not always available. Additionally, many existing multi-modality based prognostic methods commonly ignore relevant clinical variables (e.g., grade and stage), which, however, may provide supplemental information to promote the performance of model. In this paper, we propose a relation-aware shared representation learning method for prognosis analysis of cancers, which makes full use of clinical information and incomplete multi-modality data. The proposed method learns multi-modal shared space tailored for prognostic model via a dual mapping. Within the shared space, it equips with relational regularizers to explore the potential relations (i.e., feature-label and feature-feature relations) among multi-modality data for inducing discriminatory representations and simultaneously obtaining extra sparsity for alleviating overfitting. Moreover, it regresses and incorporates multiple auxiliary clinical attributes with dynamic coefficients to meliorate performance. Furthermore, in training stage, a partial mapping strategy is employed to extend and train a more reliable model with incomplete multi-modality data. We have evaluated our method on three public datasets derived from The Cancer Genome Atlas (TCGA) project, and the experimental results demonstrate the superior performance of the proposed method.