解决医学影像缺模问题，提升脑肿瘤分割精度与鲁棒性
Bridging the Gap in Missing Modalities: Leveraging Knowledge Distillation and Style Matching for Brain Tumor Segmentation
- 通过多尺度变换器蒸馏和双模式逻辑蒸馏，实现跨模态知识迁移
- 在BraTS和FeTS 2024数据集上，Dice和HD95指标均优于现有方法
- 特别适用于模态缺失场景，适合临床实际应用
准确可靠的脑肿瘤分割在医学图像分析中仍具挑战性，尤其当关键影像模态缺失时。现有研究未能充分解决边界分割不敏感及特征迁移不足的问题。本文提出MST-KDNet，包含多尺度变换器知识蒸馏以捕捉不同分辨率下的注意力权重、双模式逻辑蒸馏增强知识传递，以及融合特征匹配与对抗学习的全局风格匹配模块。在BraTS和FeTS 2024数据集上的全面实验表明，该模型在模态大量缺失条件下仍显著优于当前领先方法，在Dice和HD95评分上表现优异。结果证明其具备出色的鲁棒性与泛化能力，具有良好的临床应用前景。源代码已开源：https://github.com/Quanato607/MST-KDNet。
原文摘要 · Abstract (English)
Accurate and reliable brain tumor segmentation, particularly when dealing with missing modalities, remains a critical challenge in medical image analysis. Previous studies have not fully resolved the challenges of tumor boundary segmentation insensitivity and feature transfer in the absence of key imaging modalities. In this study, we introduce MST-KDNet, aimed at addressing these critical issues. Our model features Multi-Scale Transformer Knowledge Distillation to effectively capture attention weights at various resolutions, Dual-Mode Logit Distillation to improve the transfer of knowledge, and a Global Style Matching Module that integrates feature matching with adversarial learning. Comprehensive experiments conducted on the BraTS and FeTS 2024 datasets demonstrate that MST-KDNet surpasses current leading methods in both Dice and HD95 scores, particularly in conditions with substantial modality loss. Our approach shows exceptional robustness and generalization potential, making it a promising candidate for real-world clinical applications. Our source code is available at https://github.com/Quanato607/MST-KDNet.
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