BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

178 related articles for article (PubMed ID: 30471821)

  • 1. 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]  

  • 2. Pattern classification for breast lesion on FFDM by integration of radiomics and deep features.
    Zhang X; Liang C; Zeng D; Jiang X; Zhong R; Lan Y; Ma J; Bai L
    Comput Med Imaging Graph; 2021 Jun; 90():101922. PubMed ID: 34049119
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. 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]  

  • 5. Computer-aided detection of breast masses on full field digital mammograms.
    Wei J; Sahiner B; Hadjiiski LM; Chan HP; Petrick N; Helvie MA; Roubidoux MA; Ge J; Zhou C
    Med Phys; 2005 Sep; 32(9):2827-38. PubMed ID: 16266097
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Quantitative comparison of clustered microcalcifications in for-presentation and for-processing mammograms in full-field digital mammography.
    Wang J; Nishikawa RM; Yang Y
    Med Phys; 2017 Jul; 44(7):3726-3738. PubMed ID: 28477395
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. 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]  

  • 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. 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]  

  • 12. 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]  

  • 13. Computer-aided mass detection in mammography: false positive reduction via gray-scale invariant ranklet texture features.
    Masotti M; Lanconelli N; Campanini R
    Med Phys; 2009 Feb; 36(2):311-6. PubMed ID: 19291970
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts.
    Pérez-Benito FJ; Signol F; Pérez-Cortés JC; Pollán M; Pérez-Gómez B; Salas-Trejo D; Casals M; Martínez I; LLobet R
    Comput Methods Programs Biomed; 2019 Aug; 177():123-132. PubMed ID: 31319940
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Combining two mammographic projections in a computer aided mass detection method.
    van Engeland S; Karssemeijer N
    Med Phys; 2007 Mar; 34(3):898-905. PubMed ID: 17441235
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset.
    Li H; Giger ML; Yuan Y; Chen W; Horsch K; Lan L; Jamieson AR; Sennett CA; Jansen SA
    Acad Radiol; 2008 Nov; 15(11):1437-45. PubMed ID: 18995194
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Robust phase-based texture descriptor for classification of breast ultrasound images.
    Cai L; Wang X; Wang Y; Guo Y; Yu J; Wang Y
    Biomed Eng Online; 2015 Mar; 14():26. PubMed ID: 25889570
    [TBL] [Abstract][Full Text] [Related]  

  • 18. False-positive reduction in computer-aided mass detection using mammographic texture analysis and classification.
    Dhahbi S; Barhoumi W; Kurek J; Swiderski B; Kruk M; Zagrouba E
    Comput Methods Programs Biomed; 2018 Jul; 160():75-83. PubMed ID: 29728249
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces.
    Chan HP; Sahiner B; Lam KL; Petrick N; Helvie MA; Goodsitt MM; Adler DD
    Med Phys; 1998 Oct; 25(10):2007-19. PubMed ID: 9800710
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Breast density classification to reduce false positives in CADe systems.
    Vállez N; Bueno G; Déniz O; Dorado J; Seoane JA; Pazos A; Pastor C
    Comput Methods Programs Biomed; 2014 Feb; 113(2):569-84. PubMed ID: 24286729
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.