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Semi-supervised Medical Image Segmentation via Query Distribution Consistency
In this paper, we propose a novel Dual KMax UX-Net framework that leverages labeled data to guide the extraction of information from unlabeled data.
Training Auto-encoders Effectively via Eliminating Task-irrelevant Input Variables
Auto-encoders are often used as building blocks of deep network classifier to learn feature extractors, but task-irrelevant information in the input data may lead to bad extractors and result in poor generalization performance of the network.
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