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.

这篇论文介绍了一种互补信息相互学习（CIML）框架，通过数学建模和处理跨模态冗余信息的负面影响，它将多模态分割任务分解成多个子任务，并通过信息传递来从其他模态中提取信息，以有效地去除冗余信息，提高验证准确性和分割效果。