  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
