IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2210.15075v1
• Date: Wed, 26 Oct 2022 23:11:02 GMT
• Title: IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation
• Authors: Hritam Basak, Soumitri Chattopadhyay, Rohit Kundu, Sayan Nag, Rammohan Mallipeddi
• Abstract summary: We extend the concept of metric learning to the segmentation task. We propose a simple convolutional projection head for obtaining dense pixel-level features. A bidirectional regularization mechanism involving two-stream regularization training is devised for the downstream task.
• Score: 3.6748639131154315
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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