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arXiv · 2210.15075

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

Hritam Basak·Soumitri Chattopadhyay·Rohit Kundu·Sayan Nag·Rammohan Mallipeddi
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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. Code available: https://github.com/hritam-98/IDEAL-ICASSP23

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Hritam Basak, Soumitri Chattopadhyay, Rohit Kundu, Sayan Nag, Rammohan Mallipeddi. 2023-03-02. IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation. https://arxiv.org/abs/2210.15075

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