169 related articles for article (PubMed ID: 31603818)
1. Development and Assessment of a New Global Mammographic Image Feature Analysis Scheme to Predict Likelihood of Malignant Cases.
Heidari M; Mirniaharikandehei S; Liu W; Hollingsworth AB; Liu H; Zheng B
IEEE Trans Med Imaging; 2020 Apr; 39(4):1235-1244. PubMed ID: 31603818
[TBL] [Abstract][Full Text] [Related]
2. Applying a new quantitative image analysis scheme based on global mammographic features to assist diagnosis of breast cancer.
Chen X; Zargari A; Hollingsworth AB; Liu H; Zheng B; Qiu Y
Comput Methods Programs Biomed; 2019 Oct; 179():104995. PubMed ID: 31443864
[TBL] [Abstract][Full Text] [Related]
3. Assessment of global and local region-based bilateral mammographic feature asymmetry to predict short-term breast cancer risk.
Li Y; Fan M; Cheng H; Zhang P; Zheng B; Li L
Phys Med Biol; 2018 Jan; 63(2):025004. PubMed ID: 29226849
[TBL] [Abstract][Full Text] [Related]
4. Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer Risk.
Tan M; Pu J; Cheng S; Liu H; Zheng B
Ann Biomed Eng; 2015 Oct; 43(10):2416-28. PubMed ID: 25851469
[TBL] [Abstract][Full Text] [Related]
5. A multi-stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four-view mammograms.
Jones MA; Sadeghipour N; Chen X; Islam W; Zheng B
Med Phys; 2023 Dec; 50(12):7670-7683. PubMed ID: 37083190
[TBL] [Abstract][Full Text] [Related]
6. A new approach to develop computer-aided detection schemes of digital mammograms.
Tan M; Qian W; Pu J; Liu H; Zheng B
Phys Med Biol; 2015 Jun; 60(11):4413-27. PubMed ID: 25984710
[TBL] [Abstract][Full Text] [Related]
7. Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions.
Tan M; Aghaei F; Wang Y; Zheng B
Phys Med Biol; 2017 Jan; 62(2):358-376. PubMed ID: 27997380
[TBL] [Abstract][Full Text] [Related]
8. Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm.
Heidari M; Khuzani AZ; Hollingsworth AB; Danala G; Mirniaharikandehei S; Qiu Y; Liu H; Zheng B
Phys Med Biol; 2018 Jan; 63(3):035020. PubMed ID: 29239858
[TBL] [Abstract][Full Text] [Related]
9. Computerized detection of breast tissue asymmetry depicted on bilateral mammograms: a preliminary study of breast risk stratification.
Wang X; Lederman D; Tan J; Wang XH; Zheng B
Acad Radiol; 2010 Oct; 17(10):1234-41. PubMed ID: 20619697
[TBL] [Abstract][Full Text] [Related]
10. Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme.
Tan M; Pu J; Zheng B
Phys Med Biol; 2014 Aug; 59(15):4357-73. PubMed ID: 25029964
[TBL] [Abstract][Full Text] [Related]
11. Applying a new computer-aided detection scheme generated imaging marker to predict short-term breast cancer risk.
Mirniaharikandehei S; Hollingsworth AB; Patel B; Heidari M; Liu H; Zheng B
Phys Med Biol; 2018 May; 63(10):105005. PubMed ID: 29667606
[TBL] [Abstract][Full Text] [Related]
12. Prediction of near-term risk of developing breast cancer using computerized features from bilateral mammograms.
Sun W; Zheng B; Lure F; Wu T; Zhang J; Wang BY; Saltzstein EC; Qian W
Comput Med Imaging Graph; 2014 Jul; 38(5):348-57. PubMed ID: 24725671
[TBL] [Abstract][Full Text] [Related]
13. Prediction of near-term breast cancer risk based on bilateral mammographic feature asymmetry.
Tan M; Zheng B; Ramalingam P; Gur D
Acad Radiol; 2013 Dec; 20(12):1542-50. PubMed ID: 24200481
[TBL] [Abstract][Full Text] [Related]
14. Fusion of quantitative imaging features and serum biomarkers to improve performance of computer-aided diagnosis scheme for lung cancer: A preliminary study.
Gong J; Liu JY; Jiang YJ; Sun XW; Zheng B; Nie SD
Med Phys; 2018 Dec; 45(12):5472-5481. PubMed ID: 30317652
[TBL] [Abstract][Full Text] [Related]
15. Computer-aided classification of mammographic masses using visually sensitive image features.
Wang Y; Aghaei F; Zarafshani A; Qiu Y; Qian W; Zheng B
J Xray Sci Technol; 2017; 25(1):171-186. PubMed ID: 27911353
[TBL] [Abstract][Full Text] [Related]
16. Applying a new bilateral mammographic density segmentation method to improve accuracy of breast cancer risk prediction.
Yan S; Wang Y; Aghaei F; Qiu Y; Zheng B
Int J Comput Assist Radiol Surg; 2017 Oct; 12(10):1819-1828. PubMed ID: 28726117
[TBL] [Abstract][Full Text] [Related]
17. Applying a Random Projection Algorithm to Optimize Machine Learning Model for Breast Lesion Classification.
Heidari M; Lakshmivarahan S; Mirniaharikandehei S; Danala G; Maryada SKR; Liu H; Zheng B
IEEE Trans Biomed Eng; 2021 Sep; 68(9):2764-2775. PubMed ID: 33493108
[TBL] [Abstract][Full Text] [Related]
18. Computerized analysis of multiple-mammographic views: potential usefulness of special view mammograms in computer-aided diagnosis.
Huo Z; Giger ML; Vyborny CJ
IEEE Trans Med Imaging; 2001 Dec; 20(12):1285-92. PubMed ID: 11811828
[TBL] [Abstract][Full Text] [Related]
19. A new and fast image feature selection method for developing an optimal mammographic mass detection scheme.
Tan M; Pu J; Zheng B
Med Phys; 2014 Aug; 41(8):081906. PubMed ID: 25086537
[TBL] [Abstract][Full Text] [Related]
20. A new near-term breast cancer risk prediction scheme based on the quantitative analysis of ipsilateral view mammograms.
Sun W; Tseng TB; Qian W; Saltzstein EC; Zheng B; Yu H; Zhou S
Comput Methods Programs Biomed; 2018 Mar; 155():29-38. PubMed ID: 29512502
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
