Dehua Li

Rong Wu, Dehua Li, Cong Zhang

Semi-supervised learning is increasingly popular in medical image segmentation due to its ability to leverage large amounts of unlabeled data to extract additional information. However, most existing semi-supervised segmentation methods focus only on extracting information from unlabeled data. 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. Our approach is based on a mutual learning strategy that incorporates two modules: 3D UX-Net as our backbone meta-architecture and KMax decoder to enhance the segmentation performance. Extensive experiments on the Atrial Segmentation Challenge dataset have shown that our method can significantly improve performance by merging unlabeled data. Meanwhile, our framework outperforms state-of-the-art semi-supervised learning methods on 10\% and 20\% labeled settings. Code located at: https://github.com/Rows21/DK-UXNet.

Hui Shen, Dehua Li, Hong Wu et al.

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. In this paper,via dropping the task-irrelevant input variables the performance of auto-encoders can be obviously improved .Specifically, an importance-based variable selection method is proposed to aim at finding the task-irrelevant input variables and dropping them.It firstly estimates importance of each variable,and then drops the variables with importance value lower than a threshold. In order to obtain better performance, the method can be employed for each layer of stacked auto-encoders. Experimental results show that when combined with our method the stacked denoising auto-encoders achieves significantly improved performance on three challenging datasets.