Self-supervised learning has emerged as a powerful tool for pretraining deep
networks on unlabeled data, prior to transfer learning of target tasks with
limited annotation. The relevance between the pretraining pretext and target
tasks is crucial to the success of transfer learning. Various pretext tasks
have been proposed to utilize properties of medical image data (e.g., three
dimensionality), which are more relevant to medical image analysis than generic
ones for natural images. However, previous work rarely paid attention to data
with anatomy-oriented imaging planes, e.g., standard cardiac magnetic resonance
imaging views. As these imaging planes are defined according to the anatomy of
the imaged organ, pretext tasks effectively exploiting this information can
pretrain the networks to gain knowledge on the organ of interest. In this work,
we propose two complementary pretext tasks for this group of medical image data
based on the spatial relationship of the imaging planes. The first is to learn
the relative orientation between the imaging planes and implemented as
regressing their intersecting lines. The second exploits parallel imaging
planes to regress their relative slice locations within a stack. Both pretext
tasks are conceptually straightforward and easy to implement, and can be
combined in multitask learning for better representation learning. Thorough
experiments on two anatomical structures (heart and knee) and representative
target tasks (semantic segmentation and classification) demonstrate that the
proposed pretext tasks are effective in pretraining deep networks for
remarkably boosted performance on the target tasks, and superior to other
recent approaches.

我们提出了两个互补的预训练任务，基于医学图像数据的空间关系，用于深度网络的预训练，结果表明这些预训练任务对目标任务有显著的提升效果，且优于其他最近的方法。