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Leveraging Fixed and Dynamic Pseudo-labels for Semi-supervised Medical Image Segmentation
To this end, we propose a novel approach where multiple pseudo-labels for the same unannotated image are used to learn from the unlabeled data: the conventional fixed pseudo-label and the newly introduced dynamic pseudo-label.
Data efficient deep learning for medical image analysis: A survey
This paper conducts a thorough review of data-efficient deep learning methods for medical image analysis.
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
In this paper, we provide a comprehensive literature review of recent deep UDA approaches in medical imaging from a technical perspective.
SPLAL: Similarity-based pseudo-labeling with alignment loss for semi-supervised medical image classification
To evaluate the performance of our proposed approach, we conduct experiments on two publicly available medical image classification benchmark datasets: the skin lesion classification (ISIC 2018) and the blood cell classification dataset (BCCD).
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