癌症是一个紧迫的公共卫生问题，也是全世界死亡的主要原因之一。用于预测癌症生存的先进计算方法的开发对于帮助临床医生制定有效的治疗策略和改善患者的生活质量至关重要。生存预测方法的最新进展表明，整合来自各种癌症相关数据（例如病理图像和基因组学）的不同信息对于提高预测准确性至关重要。尽管现有方法取得了令人鼓舞的结果，但多种癌症数据中存在模态差距和语义冗余的巨大挑战，这可能会阻碍全面整合，并对进一步增强癌症生存预测造成重大障碍。在这项研究中，我们提出了一种新颖的不可知特定模态学习（ASML）框架，用于准确的癌症生存预测。为了弥合模态差距并提供不同数据模态的全面视图，我们采用不可知论特定学习策略来学习跨模态的共性和每种模态的独特性。此外，跨模态融合网络通过模态相关性建模来集成多模态信息，并以分而治之的方式减少语义冗余。在三个 TCGA 数据集上的广泛实验结果表明，对于多个数据，ASML 比其他现有的癌症生存预测方法具有更好的性能。

Cancer is a pressing public health problem and one of the main causes of mortality worldwide. The development of advanced computational methods for predicting cancer survival is pivotal in aiding clinicians to formulate effective treatment strategies and improve patient quality of life. Recent advances in survival prediction methods show that integrating diverse information from various cancer-related data, such as pathological images and genomics, is crucial for improving prediction accuracy. Despite promising results of existing approaches, there are great challenges of modality gap and semantic redundancy presented in multiple cancer data, which could hinder the comprehensive integration and pose substantial obstacles to further enhancing cancer survival prediction. In this study, we propose a novel agnostic-specific modality learning (ASML) framework for accurate cancer survival prediction. To bridge the modality gap and provide a comprehensive view of distinct data modalities, we employ an agnostic-specific learning strategy to learn the commonality across modalities and the uniqueness of each modality. Moreover, a cross-modal fusion network is exerted to integrate multimodal information by modeling modality correlations and diminish semantic redundancy in a divide-and-conquer manner. Extensive experiment results on three TCGA datasets demonstrate that ASML reaches better performance than other existing cancer survival prediction methods for multiple data.