论文标题
无监督的跨模式医学图像分段的语义引导的分解表示
Semantic-guided Disentangled Representation for Unsupervised Cross-modality Medical Image Segmentation
论文作者
论文摘要
解开表示是一种有力的技术，可以在无监督的域适应设置中解决医学图像分析中的域移位问题。但是，以前的方法仅着重于严格的域名特征，而忽略了严格的特征，并忽略了严格的特征，是否对下游任务有意义无监督域的适应设置中的模态医疗图像分割。为了确定不同方式的有意义的领域不变特征，我们介绍了一个内容歧视器，以迫使内容表示形式嵌入到相同的空间中，并迫使一个特征歧视器来确定有意义的表示形式。我们还使用像素级别的注释来指导该编码对我们进行分割的特征，以使我们的方法有意义。在两个评估指标上的余量很大。
Disentangled representation is a powerful technique to tackle domain shift problem in medical image analysis in unsupervised domain adaptation setting.However, previous methods only focus on exacting domain-invariant feature and ignore whether exacted feature is meaningful for downstream tasks.We propose a novel framework, called semantic-guided disentangled representation (SGDR), an effective method to exact semantically meaningful feature for segmentation task to improve performance of cross modality medical image segmentation in unsupervised domain adaptation setting. To exact the meaningful domain-invariant features of different modality, we introduce a content discriminator to force the content representation to be embedded to the same space and a feature discriminator to exact the meaningful representation.We also use pixel-level annotations to guide the encoder to learn features that are meaningful for segmentation task.We validated our method on two public datasets and experiment results show that our approach outperforms the state of the art methods on two evaluation metrics by a significant margin.
