Spatially localized sparse representations for breast lesion characterization-Reference-Cited by-同舟云学术

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Spatially localized sparse representations for breast lesion characterization

Published:2020-08 Issue: Volume:123 Page:103914
ISSN:0010-4825
Container-title:Computers in Biology and Medicine
language:en
Short-container-title:Computers in Biology and Medicine

Author:

Zheng Keni,Harris Chelsea,Bakic Predrag,Makrogiannis Sokratis

Funder

National Institute of General Medical Sciences

National Institutes of Health

Federal Research and Development Matching

Delaware Economic Development Office

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference39 articles.

1. Current status of the digital database for screening mammography;Heath,1998

2. A review of automatic mass detection and segmentation in mammographic images;Oliver;Med. Image Anal.,2010

3. A statistical approach for breast density segmentation;Oliver;J. Digit. Imag.,2010

4. Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer;Verma;Expert Syst. Appl.,2010

5. Online mammographic images database for development and comparison of cad schemes;Matheus;J. Digit. Imag.,2011

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An automatic sparse-based deep cascade framework with multilayer representation for detecting breast cancer;Measurement;2024-03

2. Breast mass characterization using sparse approximations of patch-sampled deep features;Medical Imaging 2023: Computer-Aided Diagnosis;2023-04-07

3. Spatially localized sparse approximations of deep features for breast mass characterization;Mathematical Biosciences and Engineering;2023

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