PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

12 days ago - By Springer

Abstract
Background
With the development of deep learning , more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing.
Results
A lightweight and multiscale network...
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Deep Neural Networks Regularization Using a Combination of Sparsity Inducing Feature Selection Methods

12 days ago - By Springer

Abstract
Deep learning is an important subcategory of machine learning approaches in which there is a hope of replacing man-made features with fully automatic extracted features. However, in deep learning, we are generally facing a very high dimensional feature space. This may lead to overfitting problem which is tried to be prevented by applying regularization techniques. In this framework, the sparse representation based feature selection and regularization methods are very attractive. This is because of the nature of the sparse methods which represent a data with as less as possible...
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