The recent prevalence of deep neural networks has lead semantic segmentation networks to achieve human-level performance in the medical field when sufficient training data is provided. Such networks however fail to generalize when tasked with predicting semantic maps for out-of-distribution images, requiring model re-training on the new distributions. This expensive process necessitates expert knowledge in order to generate training labels. Distribution shifts can arise naturally in the medical field via the choice of imaging device, i.e. MRI or CT scanners. To combat the need for labeling images in a target domain after a model is successfully trained in a fully annotated \textit{source domain} with a different data distribution, unsupervised domain adaptation (UDA) can be used. Most UDA approaches ensure target generalization by creating a shared source/target latent feature space. This allows a source trained classifier to maintain performance on the target domain. However most UDA approaches require joint source and target data access, which may create privacy leaks with respect to patient information. We propose an UDA algorithm for medical image segmentation that does not require access to source data during adaptation, and is thus capable in maintaining patient data privacy. We rely on an approximation of the source latent features at adaptation time, and create a joint source/target embedding space by minimizing a distributional distance metric based on optimal transport. We demonstrate our approach is competitive to recent UDA medical segmentation works even with the added privacy requisite.
翻译:最近深心神经网络的流行导致在提供足够培训数据的情况下,在提供足够培训数据的情况下,在医疗领域实现人类水平性能的语义分割网中,最近普遍存在的深层神经网络导致语义分割网,在提供足够培训数据的情况下,在医疗领域实现人文水平业绩。然而,这些网络在负责预测用于传播外图像的语义图时,却未能普遍化,要求对新分布进行示范性再培训。这一昂贵的过程需要专家知识才能产生培训标签。在医疗领域,通过选择成像设备,即磁共振仪或CT扫描仪,可以自然地产生分布变化。在对一个目标领域,在模型经过充分注解后,在对一个具有不同数据分布的模型进行充分说明后,在医疗领域进行不受监督的域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域名的标记需要加以成功训练,但需要获得最新的源域域域域域域域域域域图数据,可以加以利用。