Consecutive Knowledge Meta-Adaptation Learning for Unsupervised Medical Diagnosis. (arXiv:2209.10425v1 [eess.IV])

Deep learning-based Computer-Aided Diagnosis (CAD) has attracted appealing
attention in academic researches and clinical applications. Nevertheless, the
Convolutional Neural Networks (CNNs) diagnosis system heavily relies on the
well-labeled lesion dataset, and the sensitivity to the variation of data
distribution also restricts the potential application of CNNs in CAD.
Unsupervised Domain Adaptation (UDA) methods are developed to solve the
expensive annotation and domain gaps problem and have achieved remarkable
success in medical image analysis. Yet existing UDA approaches only adapt
knowledge …

arxiv diagnosis knowledge medical meta unsupervised