Radiologists must utilize multiple modal images for tumor segmentation and diagnosis due to the limitations of medical imaging and the diversity of tumor signals. This leads to the development of multimodal learning in segmentation. However, the redundancy among modalities creates challenges for existing subtraction-based joint learning methods, such as misjudging the importance of modalities, ignoring specific modal information, and increasing cognitive load. These thorny issues ultimately decrease segmentation accuracy and increase the risk of overfitting. This paper presents the complementary information mutual learning (CIML) framework, which can mathematically model and address the negative impact of inter-modal redundant information. CIML adopts the idea of addition and removes inter-modal redundant information through inductive bias-driven task decomposition and message passing-based redundancy filtering. CIML first decomposes the multimodal segmentation task into multiple subtasks based on expert prior knowledge, minimizing the information dependence between modalities. Furthermore, CIML introduces a scheme in which each modality can extract information from other modalities additively through message passing. To achieve non-redundancy of extracted information, the redundant filtering is transformed into complementary information learning inspired by the variational information bottleneck. The complementary information learning procedure can be efficiently solved by variational inference and cross-modal spatial attention. Numerical results from the verification task and standard benchmarks indicate that CIML efficiently removes redundant information between modalities, outperforming SOTA methods regarding validation accuracy and segmentation effect.

翻译：由于医学影像的局限性以及肿瘤信号的多样性，放射科医生必须利用多模态图像进行肿瘤分割与诊断，这推动了分割任务中多模态学习的发展。然而，模态间的冗余信息给现有基于减法的联合学习方法带来了挑战，例如误判模态重要性、忽略特定模态信息以及增加认知负荷。这些棘手问题最终降低了分割精度，并增加了过拟合风险。本文提出互补信息互学习（CIML）框架，可从数学上建模并解决模态间冗余信息的负面影响。CIML采用加法思想，通过归纳偏置驱动的任务分解与基于消息传递的冗余过滤来消除模态间冗余信息。首先，基于专家先验知识将多模态分割任务分解为多个子任务，最小化模态间的信息依赖；其次，引入各模态通过消息传递从其他模态中加法式提取信息的机制。为实现提取信息的非冗余性，基于变分信息瓶颈将冗余过滤转化为互补信息学习。互补信息学习过程可通过变分推理与跨模态空间注意力高效求解。验证任务与标准基准的数值结果表明，CIML能有效消除模态间冗余信息，在验证精度与分割效果上均优于当前最优方法。