用于多模态癌症生存预测的原型信息瓶颈与解缠方法
Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Prediction
AI总结：
本文提出PIBD框架，通过原型信息瓶颈模块解决模态内冗余、原型信息解缠模块解决模态间冗余，在多模态癌症生存预测中提取紧凑判别性信息，并在五个癌症基准数据集上验证了优越性。
摘要：
多模态学习显著有利于癌症生存预测，尤其是病理图像与基因组数据的整合。尽管多模态学习在癌症生存预测中具有优势，但多模态数据中的大量冗余阻碍了其提取具有判别性和紧凑性的信息：（1）大量模态内与任务无关的信息模糊了判别性，尤其是对于病理学中具有大量图像块的十亿像素级全切片图像（WSIs）以及基因组数据中的数千条通路，导致“模态内冗余”问题。（2）模态之间重复的信息主导了多模态数据的表示，使得模态特异性信息容易被忽略，从而产生“模态间冗余”问题。为了解决这些问题，我们提出了一个新的框架，即原型信息瓶颈与解缠（PIBD），它由用于模态内冗余的原型信息瓶颈（PIB）模块和用于模态间冗余的原型信息解缠（PID）模块组成。具体而言，我们提出了信息瓶颈的一种变体PIB，用于对不同风险水平下近似一组实例的原型进行建模，这些原型可用于在模态内选择具有判别性的实例。PID模块在联合原型分布的指导下，将纠缠的多模态数据解耦为紧凑且不同的成分：模态共有知识和模态特异性知识。在五个癌症基准数据集上的大量实验证明了我们方法优于其他方法。
英文摘要：
Multimodal learning significantly benefits cancer survival prediction, especially the integration of pathological images and genomic data. Despite advantages of multimodal learning for cancer survival prediction, massive redundancy in multimodal data prevents it from extracting discriminative and compact information: (1) An extensive amount of intra-modal task-unrelated information blurs discriminability, especially for gigapixel whole slide images (WSIs) with many patches in pathology and thousands of pathways in genomic data, leading to an ``intra-modal redundancy" issue. (2) Duplicated information among modalities dominates the representation of multimodal data, which makes modality-specific information prone to being ignored, resulting in an ``inter-modal redundancy" issue. To address these, we propose a new framework, Prototypical Information Bottlenecking and Disentangling (PIBD), consisting of Prototypical Information Bottleneck (PIB) module for intra-modal redundancy and Prototypical Information Disentanglement (PID) module for inter-modal redundancy. Specifically, a variant of information bottleneck, PIB, is proposed to model prototypes approximating a bunch of instances for different risk levels, which can be used for selection of discriminative instances within modality. PID module decouples entangled multimodal data into compact distinct components: modality-common and modality-specific knowledge, under the guidance of the joint prototypical distribution. Extensive experiments on five cancer benchmark datasets demonstrated our superiority over other methods.
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