173 related articles for article (PubMed ID: 29431858)
1. A new computer-aided detection approach based on analysis of local and global mammographic feature asymmetry.
Kelder A; Lederman D; Zheng B; Zigel Y
Med Phys; 2018 Apr; 45(4):1459-1470. PubMed ID: 29431858
[TBL] [Abstract][Full Text] [Related]
2. A new computer-aided detection scheme based on assessment of local bilateral mammographic feature asymmetry - a preliminary evaluation.
Kelder A; Zigel Y; Lederman D; Zheng B
Annu Int Conf IEEE Eng Med Biol Soc; 2015; 2015():6394-7. PubMed ID: 26737756
[TBL] [Abstract][Full Text] [Related]
3. Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography.
Samala RK; Chan HP; Hadjiiski L; Helvie MA; Wei J; Cha K
Med Phys; 2016 Dec; 43(12):6654. PubMed ID: 27908154
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.
Qiu Y; Yan S; Gundreddy RR; Wang Y; Cheng S; Liu H; Zheng B
J Xray Sci Technol; 2017; 25(5):751-763. PubMed ID: 28436410
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy.
Aghaei F; Tan M; Hollingsworth AB; Zheng B
J Magn Reson Imaging; 2016 Nov; 44(5):1099-1106. PubMed ID: 27080203
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. Applying a new maximum local asymmetry feature analysis method to improve near-term breast cancer risk prediction.
Yan S; Zhang L; Song C
Phys Med Biol; 2018 Oct; 63(20):205010. PubMed ID: 30255850
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. 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]
14. A computer-aided diagnosis scheme of breast lesion classification using GLGLM and shape features: Combined-view and multi-classifiers.
Liang C; Bian Z; Lv W; Chen S; Zeng D; Ma J
Phys Med; 2018 Nov; 55():61-72. PubMed ID: 30471821
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment.
Zheng Y; Keller BM; Ray S; Wang Y; Conant EF; Gee JC; Kontos D
Med Phys; 2015 Jul; 42(7):4149-60. PubMed ID: 26133615
[TBL] [Abstract][Full Text] [Related]
18. An Improved CAD System for Breast Cancer Diagnosis Based on Generalized Pseudo-Zernike Moment and Ada-DEWNN Classifier.
Singh SP; Urooj S
J Med Syst; 2016 Apr; 40(4):105. PubMed ID: 26892455
[TBL] [Abstract][Full Text] [Related]
19. Assessment of performance and reproducibility of applying a content-based image retrieval scheme for classification of breast lesions.
Gundreddy RR; Tan M; Qiu Y; Cheng S; Liu H; Zheng B
Med Phys; 2015 Jul; 42(7):4241-9. PubMed ID: 26133622
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
