利用扫描仪元数据提升脑肿瘤图像分析精度，解决模态缺失问题。
Meta-D: Metadata-Aware Architectures for Brain Tumor Analysis and Missing-Modality Segmentation
- 用扫描序列和方位元数据动态调节卷积特征提取
- 2D检测F1-score提升最高2.62%，3D分割Dice提升5.12%
- 适合处理模态缺失场景，模型参数减少24.1%
我们提出Meta-D，一种显式利用MRI扫描序列（如T1、T2）和方位（如轴向）等类别型元数据来引导脑肿瘤分析特征提取的架构。通过在2D肿瘤检测中注入元数据动态调制卷积特征，相比仅使用图像的基线模型，F1-score最高提升2.62%。由于元数据能在数据可用时稳定特征表示，我们假设其可作为数据缺失时的稳健锚点。因此，我们将该方法应用于3D模态缺失肿瘤分割任务，采用基于元数据的交叉注意力机制，使Transformer Maximizer能识别并路由有效模态，确保网络聚焦于可用切片。该方法在极端模态缺失下，脑肿瘤分割Dice分数最高提升5.12%，同时模型参数减少24.1%。
原文摘要 · Abstract (English)
We present Meta-D, an architecture that explicitly leverages categorical scanner metadata such as MRI sequence and plane orientation to guide feature extraction for brain tumor analysis. We aim to improve the performance of medical image deep learning pipelines by integrating explicit metadata to stabilize feature representations. We first evaluate this in 2D tumor detection, where injecting sequence (e.g., T1, T2) and plane (e.g., axial) metadata dynamically modulates convolutional features, yielding an absolute increase of up to 2.62% in F1-score over image-only baselines. Because metadata grounds feature extraction when data are available, we hypothesize it can serve as a robust anchor when data are missing. We apply this to 3D missing-modality tumor segmentation. Our Transformer Maximizer utilizes metadata-based cross-attention to isolate and route available modalities, ensuring the network focuses on valid slices. This targeted attention improves brain tumor segmentation Dice scores by up to 5.12% under extreme modality scarcity while reducing model parameters by 24.1%.
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