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Abstract: Semi-supervised medical image segmentation reduces annotation cost by leveraging abundant unlabeled data, but its performance is often limited by unreliable pseudo-labels in anatomically ...
Abstract: Semi-supervised learning methods, compared to fully supervised learning, offer significant potential to alleviate the burden of manual annotations on clinicians. By leveraging unlabeled data ...
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