  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.
