Sparse Data-Driven Learning for Effective and Efficient Biomedical Image Segmentation
Onofrey JA, Staib LH, Huang X, Zhang F, Papademetris X, Metaxas D, Rueckert D, Duncan JS (2020)
Publication Type: Book chapter / Article in edited volumes
Publication year: 2020
Journal
Publisher: Annual Reviews Inc.
Series: Annual Review of Biomedical Engineering
City/Town: Palo Alto
Book Volume: 22
Pages Range: 127-153
DOI: 10.1146/annurev-bioeng-060418-052147
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.
Involved external institutions
Yale University
United States (USA) (US)
The State University of New Jersey (Rutgers)
United States (USA) (US)
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
United States (USA) (US)
The State University of New Jersey (Rutgers)
United States (USA) (US)
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
How to cite
APA:
Onofrey, J.A., Staib, L.H., Huang, X., Zhang, F., Papademetris, X., Metaxas, D.,... Duncan, J.S. (2020). Sparse Data-Driven Learning for Effective and Efficient Biomedical Image Segmentation. In (pp. 127-153). Palo Alto: Annual Reviews Inc..
MLA:
Onofrey, John A., et al. "Sparse Data-Driven Learning for Effective and Efficient Biomedical Image Segmentation." Palo Alto: Annual Reviews Inc., 2020. 127-153.
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