Convolutional networks (ConvNets) have achieved great successes in various challenging vision tasks. However, the performance of ConvNets would degrade when encountering the domain shift. The domain adaptation is more significant while challenging in the field of biomedical image analysis, where cross-modality data have largely different distributions. Given that annotating the medical data is especially expensive, the supervised transfer learning approaches are not quite optimal. In this paper, we propose an unsupervised domain adaptation framework with adversarial learning for cross-modality biomedical image segmentations. Specifically, our model is based on a dilated fully convolutional network for pixel-wise prediction. Moreover, we build a plug-and-play domain adaptation module (DAM) to map the target input to features which are aligned with source domain feature space. A domain critic module (DCM) is set up for discriminating the feature space of both domains. We optimize the DAM and DCM via an adversarial loss without using any target domain label. Our proposed method is validated by adapting a ConvNet trained with MRI images to unpaired CT data for cardiac structures segmentations, and achieved very promising results.


翻译:然而,ConvNet的性能在遇到域变换时会下降。在生物医学图像分析领域,跨模式数据分布大相径庭,在生物医学图像分析领域,跨模式数据分布大相径庭,这个领域适应性更为重要,同时在生物医学图像分析领域更具挑战性。鉴于医疗数据说明特别昂贵,监督传输学习方法并不尽善尽美。在本文件中,我们提议建立一个不受监督的域适应框架,对跨模式生物医学图像分割进行对抗性学习。具体地说,我们的模型基于对像素预测的扩展式全动态网络。此外,我们建立了一个插和功能域适应模块,以绘制与源域域特性空间相匹配的特征的目标输入图。一个域评论模块(DCM)是用来区分这两个域的特征空间的。我们通过对抗性损失优化DAM和DCMM,而没有使用任何目标域标。我们提议的方法得到验证,方法是调整一个ConvNet,用 MRI 图像培训的CRIS 图像转换成未对心脏结构断段数据,并取得了很有希望的结果。

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