# PASSION: Towards Effective Incomplete Multi-Modal Medical Image Segmentation with Imbalanced Missing Rates

arXiv: https://arxiv.org/abs/2407.14796 · HTML: https://mlsanity.com/paper/2407.14796/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Distillation for medical imaging, one of 12 directions in Knowledge distillation.
- Builds on 24 of its 48 references that are in the map.
- First posted on arXiv 20 Jul 2024.

## Placed in

- Knowledge distillation / [Distillation for medical imaging](https://mlsanity.com/distill/distillation-for-medical-imaging/)

## Builds on (most cited first, 24 of 24)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Long-tail learning via logit adjustment](https://mlsanity.com/paper/2007.07314/) (2020)
- [What Makes Training Multi-Modal Classification Networks Hard?](https://mlsanity.com/paper/1905.12681/) (2019)
- [Balanced Multimodal Learning via On-the-fly Gradient Modulation](https://mlsanity.com/paper/2203.15332/) (2022)
- [SMIL: Multimodal Learning with Severely Missing Modality](https://mlsanity.com/paper/2103.05677/) (2021)
- [Multi-modal Learning with Missing Modality via Shared-Specific Feature Modelling](https://mlsanity.com/paper/2307.14126/) (2023)
- [Audiovisual SlowFast Networks for Video Recognition](https://mlsanity.com/paper/2001.08740/) (2020)
- [Brain Tumor Segmentation with Deep Neural Networks](https://mlsanity.com/paper/1505.03540/) (2015)
- [Multivariate mixture model for myocardium segmentation combining multi-source images](https://mlsanity.com/paper/1612.08820/) (2016)
- [PMR: Prototypical Modal Rebalance for Multimodal Learning](https://mlsanity.com/paper/2211.07089/) (2022)
- [HeMIS: Hetero-Modal Image Segmentation](https://mlsanity.com/paper/1607.05194/) (2016)
- [Missing MRI Pulse Sequence Synthesis using Multi-Modal Generative Adversarial Network](https://mlsanity.com/paper/1904.12200/) (2019)
- [Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation](https://mlsanity.com/paper/1907.11150/) (2019)
- [Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion](https://mlsanity.com/paper/2002.09708/) (2020)
- [M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities](https://mlsanity.com/paper/2303.05302/) (2023)
- [ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities](https://mlsanity.com/paper/2106.14591/) (2021)
- [Improving Multi-Modal Learning with Uni-Modal Teachers](https://mlsanity.com/paper/2106.11059/) (2021)
- [SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities](https://mlsanity.com/paper/2204.02961/) (2022)
- [Prototype Knowledge Distillation for Medical Segmentation with Missing Modality](https://mlsanity.com/paper/2303.09830/) (2023)
- [MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR Images](https://mlsanity.com/paper/2211.03062/) (2022)
- [RedCore: Relative Advantage Aware Cross-modal Representation Learning for Missing Modalities with Imbalanced Missing Rates](https://mlsanity.com/paper/2312.10386/) (2023)

## Built on by (most cited first, 17 of 17)

- [Deep Multimodal Learning with Missing Modality: A Survey](https://mlsanity.com/paper/2409.07825/) (2024)
- [Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing Modalities](https://mlsanity.com/paper/2505.16809/) (2025)
- [MCMoE: Completing Missing Modalities with Mixture of Experts for Incomplete Multimodal Action Quality Assessment](https://mlsanity.com/paper/2511.17397/) (2025)
- [MCE: Towards a General Framework for Handling Missing Modalities under Imbalanced Missing Rates](https://mlsanity.com/paper/2510.10534/) (2025)
- [UniMRSeg: Unified Modality-Relax Segmentation via Hierarchical Self-Supervised Compensation](https://mlsanity.com/paper/2509.16170/) (2025)
- [CHARM: Collaborative Harmonization across Arbitrary Modalities for Modality-agnostic Semantic Segmentation](https://mlsanity.com/paper/2508.03060/) (2025)
- [Towards Robust Optical-SAR Object Detection under Missing Modalities: A Dynamic Quality-Aware Fusion Framework](https://mlsanity.com/paper/2512.22447/) (2025)
- [Boomda: Balanced Multi-objective Optimization for Multimodal Domain Adaptation](https://mlsanity.com/paper/2511.08152/) (2025)
- [MGML: A Plug-and-Play Meta-Guided Multi-Modal Learning Framework for Incomplete Multimodal Brain Tumor Segmentation](https://mlsanity.com/paper/2512.23936/) (2025)
- [SGMA: Semantic-Guided Modality-Aware Segmentation for Remote Sensing with Incomplete Multimodal Data](https://mlsanity.com/paper/2603.02505/) (2026)
- [MissBench: Benchmarking Multimodal Affective Analysis under Imbalanced Missing Modalities](https://mlsanity.com/paper/2603.09874/) (2026)
- [BALM: A Model-Agnostic Framework for Balanced Multimodal Learning under Imbalanced Missing Rates](https://mlsanity.com/paper/2603.19718/) (2026)
- [DMAF-Net: An Effective Modality Rebalancing Framework for Incomplete Multi-Modal Medical Image Segmentation](https://mlsanity.com/paper/2506.11691/) (2025)
- [A Robust Incomplete Multimodal Low-Rank Adaptation Approach for Emotion Recognition](https://mlsanity.com/paper/2507.11202/) (2025)
- [GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences](https://mlsanity.com/paper/2605.23183/) (2026)
- [Spatio-Temporal Mixture-of-Modality-Experts Diffusion for Quantitative DCE-MRI Synthesis from Incomplete MR Sequences](https://mlsanity.com/paper/2606.25535/) (2026)
- [LASSNet: Level-Aware Availability-Conditioned Spatial-Semantic Fusion for Brain Tumor Segmentation with Missing MRI Modalities](https://mlsanity.com/paper/2609.06733/) (2026)

## Abstract (arXiv)

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.