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. Code available: https://github.com/hritam-98/IDEAL-ICASSP23

翻译：由于标注数据的稀缺性，对比自监督学习框架近年来在多项医学图像分析任务中展现出巨大潜力。然而，现有对比机制因缺乏对局部特征的挖掘能力，在密集像素级分割任务中表现欠佳。为此，我们将度量学习的概念扩展至分割任务，通过密集（非）相似性学习预训练深层编码器网络，并采用半监督范式对下游任务进行微调。具体而言，我们提出一种简单的卷积投影头以获取密集像素级特征，并设计新的对比损失函数来利用这些密集投影，从而改善局部表征。针对下游任务，我们设计了一种基于双流模型训练的双向一致性正则化机制。实验对比表明，我们的IDEAL方法在心脏MRI分割任务上以显著优势超越现有最先进方法。代码地址：https://github.com/hritam-98/IDEAL-ICASSP23