The paradigm of self-supervision focuses on representation learning from raw data without the need of labor-consuming annotations, which is the main bottleneck of current data-driven methods. Self-supervision tasks are often used to pre-train a neural network with a large amount of unlabeled data and extract generic features of the dataset. The learned model is likely to contain useful information which can be transferred to the downstream main task and improve performance compared to random parameter initialization. In this paper, we propose a new self-supervision task called source identification (SI), which is inspired by the classic blind source separation problem. Synthetic images are generated by fusing multiple source images and the network's task is to reconstruct the original images, given the fused images. A proper understanding of the image content is required to successfully solve the task. We validate our method on two medical image segmentation tasks: brain tumor segmentation and white matter hyperintensities segmentation. The results show that the proposed SI task outperforms traditional self-supervision tasks for dense predictions including inpainting, pixel shuffling, intensity shift, and super-resolution. Among variations of the SI task fusing images of different types, fusing images from different patients performs best.

翻译：自监督学习范式侧重于从原始数据中学习表征，无需依赖耗时的人工标注——而人工标注正是当前数据驱动方法的主要瓶颈。自监督任务通常用于利用大量无标签数据预训练神经网络，并提取数据集中的通用特征。相比随机参数初始化，习得的模型更可能包含可迁移至下游主要任务的有用信息，从而提升性能。本文提出一种名为"源识别（SI）"的新型自监督任务，该任务受经典盲源分离问题启发。通过融合多个源图像生成合成图像，网络的任务是根据融合图像重建原始图像。成功解决该任务需要对图像内容有恰当理解。我们在两项医学图像分割任务上验证了本方法：脑肿瘤分割和脑白质高信号分割。结果表明，所提出的SI任务在密集预测任务中优于传统自监督任务（包括图像修复、像素混洗、强度偏移和超分辨率）。在不同类型图像融合的SI任务变体中，融合不同患者的图像表现最佳。