通过特征对齐提升缺失模态脑肿瘤分割性能
Mind the Gap: Promoting Missing Modality Brain Tumor Segmentation with Alignment
- 用分布锚点对齐不同模态的潜在特征
- 使教师模型模态差距缩小，学生模型骰子系数平均提升1.75
- 适合临床中多模态数据缺失场景的分割任务
脑肿瘤分割通常依赖多种磁共振成像（MRI）模态，但临床实践中某些模态可能缺失，带来更大挑战。知识蒸馏成为应对策略之一，但现有方法常忽视模态差异，难以学习跨模态的不变特征表示，导致师生模型性能受限。本文提出一种新范式：将参与模态的潜在特征对齐至一个明确的分布锚点。主要贡献在于证明该训练范式能保证紧致的证据下界，理论证明其有效性。在不同主干网络上的大量实验表明，该方法可实现不变特征表示，使教师模型的模态差距缩小，进而为缺失模态的学生模型提供更优指导，平均骰子系数提升1.75。
原文摘要 · Abstract (English)
Brain tumor segmentation is often based on multiple magnetic resonance imaging (MRI). However, in clinical practice, certain modalities of MRI may be missing, which presents an even more difficult scenario. To cope with this challenge, knowledge distillation has emerged as one promising strategy. However, recent efforts typically overlook the modality gaps and thus fail to learn invariant feature representations across different modalities. Such drawback consequently leads to limited performance for both teachers and students. To ameliorate these problems, in this paper, we propose a novel paradigm that aligns latent features of involved modalities to a well-defined distribution anchor. As a major contribution, we prove that our novel training paradigm ensures a tight evidence lower bound, thus theoretically certifying its effectiveness. Extensive experiments on different backbones validate that the proposed paradigm can enable invariant feature representations and produce a teacher with narrowed modality gaps. This further offers superior guidance for missing modality students, achieving an average improvement of 1.75 on dice score.
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