  Incomplete multi-modal image segmentation is a fundamental task in medical
imaging to refine deployment efficiency when only partial modalities are
available. However, the common practice that complete-modality data is visible
during model training is far from realistic, as modalities can have imbalanced
missing rates in clinical scenarios. In this paper, we, for the first time,
formulate such a challenging setting and propose Preference-Aware
Self-diStillatION (PASSION) for incomplete multi-modal medical image
segmentation under imbalanced missing rates. Specifically, we first construct
pixel-wise and semantic-wise self-distillation to balance the optimization
objective of each modality. Then, we define relative preference to evaluate the
dominance of each modality during training, based on which to design task-wise
and gradient-wise regularization to balance the convergence rates of different
modalities. Experimental results on two publicly available multi-modal datasets
demonstrate the superiority of PASSION against existing approaches for modality
balancing. More importantly, PASSION is validated to work as a plug-and-play
module for consistent performance improvement across different backbones. Code
is available at https://github.com/Jun-Jie-Shi/PASSION.
