Due to the scarcity of labeled data, Contrastive Self-Supervised Learning (SSL) frameworks have lately shown great potential in several medical image analysis tasks. However, the existing contrastive mechanisms are sub-optimal for dense pixel-level segmentation tasks due to their inability to mine local features. To this end, we extend the concept of metric learning to the segmentation task, using a dense (dis)similarity learning for pre-training a deep encoder network, and employing a semi-supervised paradigm to fine-tune for the downstream task. Specifically, we propose a simple convolutional projection head for obtaining dense pixel-level features, and a new contrastive loss to utilize these dense projections thereby improving the local representations. A bidirectional consistency regularization mechanism involving two-stream model training is devised for the downstream task. Upon comparison, our IDEAL method outperforms the SoTA methods by fair margins on cardiac MRI segmentation.
翻译:由于缺乏标签数据,自学框架最近在一些医学图像分析任务中显示出巨大的潜力,然而,现有的对比机制对于密集像素级分解任务来说是次最佳的,因为它们无法挖掘当地特征。 为此,我们将衡量学习的概念扩大到分解任务,在深层编码网络的训练前采用密集(不同)不同学习,并采用半监督模式微调下游任务。具体地说,我们提议为获得密集像素级特征而设置简单的同级投影头,并提议利用这些密集的投影来改进当地的表示方式而造成新的反差损失。为下游任务设计了双向一致性正规化机制,包括双向模式培训。比较后,我们的IDAL方法在心脏MRI分解上通过公平边距优于 SoTA方法。