论文标题
通过潜在的多源相关表示，脑肿瘤分割，缺失方式缺失
Brain tumor segmentation with missing modalities via latent multi-source correlation representation
论文作者
论文摘要
多模式MR图像可以提供互补信息，以进行精确的脑肿瘤分割。但是，在临床实践中缺少成像方式是很常见的。由于多模态之间存在很强的相关性，因此提出了一个新型的相关表示块，以特别发现潜在的多源相关性。由于获得的相关表示形式，因此在缺失模式的情况下，分割变得更加健壮。模型参数估计模块首先映射每个编码者产生的单个表示以获取独立参数，然后，在这些参数下，相关表达式模块将转换所有单个表示形式以形成潜在的多源相关表示。最后，通过注意机制将跨模式的相关表示融合为共享表示，以强调分割的最重要特征。我们在Brats 2018数据集上评估了模型，它的表现要优于当前的最新方法，并在缺少一种或多种模式时会产生强大的结果。
Multimodal MR images can provide complementary information for accurate brain tumor segmentation. However, it's common to have missing imaging modalities in clinical practice. Since there exists a strong correlation between multi modalities, a novel correlation representation block is proposed to specially discover the latent multi-source correlation. Thanks to the obtained correlation representation, the segmentation becomes more robust in the case of missing modalities. The model parameter estimation module first maps the individual representation produced by each encoder to obtain independent parameters, then, under these parameters, the correlation expression module transforms all the individual representations to form a latent multi-source correlation representation. Finally, the correlation representations across modalities are fused via the attention mechanism into a shared representation to emphasize the most important features for segmentation. We evaluate our model on BraTS 2018 datasets, it outperforms the current state-of-the-art method and produces robust results when one or more modalities are missing.
