针对脑肿瘤分割难题，提出区域感知融合与自适应提示机制。
Sub-Region-Aware Modality Fusion and Adaptive Prompting for Multi-Modal Brain Tumor Segmentation
- 按肿瘤不同区域动态融合多模态影像信息
- 在坏死核心区域分割准确率显著提升
- 适合需要高精度医学图像分割的研究者
将基础模型适配于多模态医学影像是一项关键但尚未解决的挑战。现有模型常难以有效融合多源信息，且难以适应病灶组织的异质性。为此，我们提出一种新框架，包含两项核心技术：子区域感知的模态注意力机制和自适应提示工程。该注意力机制使模型能够为每个肿瘤子区域学习最优的模态组合方式，而自适应提示策略则利用基础模型的内在能力提升分割精度。我们在BraTS 2020脑肿瘤分割数据集上验证了该框架，结果表明其显著优于基线方法，尤其在具有挑战性的坏死核心子区域表现突出。本工作提供了一种系统且高效的方法，推动基于基础模型的医学影像分析向更精准、更鲁棒的方向发展。
原文摘要 · Abstract (English)
The successful adaptation of foundation models to multi-modal medical imaging is a critical yet unresolved challenge. Existing models often struggle to effectively fuse information from multiple sources and adapt to the heterogeneous nature of pathological tissues. To address this, we introduce a novel framework for adapting foundation models to multi-modal medical imaging, featuring two key technical innovations: sub-region-aware modality attention and adaptive prompt engineering. The attention mechanism enables the model to learn the optimal combination of modalities for each tumor sub-region, while the adaptive prompting strategy leverages the inherent capabilities of foundation models to refine segmentation accuracy. We validate our framework on the BraTS 2020 brain tumor segmentation dataset, demonstrating that our approach significantly outperforms baseline methods, particularly in the challenging necrotic core sub-region. Our work provides a principled and effective approach to multi-modal fusion and prompting, paving the way for more accurate and robust foundation model-based solutions in medical imaging.
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