John A Onofrey, Lawrence H Staib, Xiaojie Huang, Fan Zhang, Xenophon Papademetris, Dimitris Metaxas, Daniel Rueckert, James S Duncan
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引用次数: 4
Abstract
Sparsity is a powerful concept to exploit for high-dimensional machine learning and associated representational and computational efficiency. Sparsity is well suited for medical image segmentation. We present a selection of techniques that incorporate sparsity, including strategies based on dictionary learning and deep learning, that are aimed at medical image segmentation and related quantification.
期刊介绍：
Since 1999, the Annual Review of Biomedical Engineering has been capturing major advancements in the expansive realm of biomedical engineering. Encompassing biomechanics, biomaterials, computational genomics and proteomics, tissue engineering, biomonitoring, healthcare engineering, drug delivery, bioelectrical engineering, biochemical engineering, and biomedical imaging, the journal remains a vital resource. The current volume has transitioned from gated to open access through Annual Reviews' Subscribe to Open program, with all articles published under a CC BY license.
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