To realize the full potential of deep learning for medical imaging, large
annotated datasets are required for training. Such datasets are difficult to
acquire because labeled medical images are not usually available due to privacy
issues, lack of experts available for annotation, underrepresentation of rare
conditions and poor standardization. Lack of annotated data has been addressed
in conventional vision applications using synthetic images refined via
unsupervised adversarial training to look like real images. However, this
approach is difficult to extend to general medical imaging because of the
complex and diverse set of features found in real human tissues. We propose an
alternative framework that uses a reverse flow, where adversarial training is
used to make real medical images more like synthetic images, and hypothesize
that clinically-relevant features can be preserved via self-regularization.
These domain-adapted images can then be accurately interpreted by networks
trained on large datasets of synthetic medical images. We test this approach
for the notoriously difficult task of depth-estimation from endoscopy. We train
a depth estimator on a large dataset of synthetic images generated using an
accurate forward model of an endoscope and an anatomically-realistic colon.
This network predicts significantly better depths when using synthetic-like
domain-adapted images compared to the real images, confirming that the
clinically-relevant features of depth are preserved.

提出了一种采用反向流的替代框架，其中采用对抗性训练使真实医学图像更像合成图像，并假设通过自我正则化可以保留临床相关特征。用这种方法解决了内窥镜深度估计这一困难任务，证明了这种领域自适应图像可以通过训练用合成图像构建的网络来准确地解释。