When Fusion Fails: Corruption-Aware Rebalanced Fusion for Multi-Modal Medical Image Segmentation

2026-09-09

AI Summary

为解决多模态医学图像分割中因质量差异导致的融合失败问题，提出CoReFuse-Med框架，通过抑制特征传输中的损坏并重新平衡模态贡献来提高准确性和鲁棒性。

Abstract

Multi-modal medical image segmentation leverages complementary diagnostic information, yet fusion can underperform single-modality baselines when spatially aligned inputs differ in quality. Here, "corruption" primarily denotes resolution-induced degradation rather than misalignment or complete modality absence, while synthetic noise is evaluated only as an auxiliary setting. We identify a critical optimization-inference inconsistency: degraded modalities can receive weak training updates yet substantially affect predictions, indicating active interference with fusion. We attribute this failure to resampling-induced feature corruption and optimization bias, where noisy features propagate through skip connections and encourage unreliable modality selection. We therefore propose CoReFuse-Med, a Corruption-aware Rebalanced Fusion framework that suppresses corruption during feature transmission and rebalances modality contributions during high-level fusion. Experiments on EPVS, BraTS, and WMH, including multiple Z-axis slice-retention ratios and an auxiliary noise test, demonstrate improved accuracy and robustness under modality-quality discrepancies. Our code is available at https://github.com/lrever/CoReFuse.

Problem

• multi-modal medical image segmentation
• corruption
• fusion
• degradation
• optimization-inference inconsistency

Innovation

• Corruption-aware Rebalanced Fusion
• multi-modal medical image segmentation
• feature corruption
• optimization bias
• skip connections