BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

672 related articles for article (PubMed ID: 28436410)

  • 1. 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]  

  • 2. 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]  

  • 3. A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification.
    Al-Antari MA; Al-Masni MA; Choi MT; Han SM; Kim TS
    Int J Med Inform; 2018 Sep; 117():44-54. PubMed ID: 30032964
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system.
    Al-Masni MA; Al-Antari MA; Park JM; Gi G; Kim TY; Rivera P; Valarezo E; Choi MT; Han SM; Kim TS
    Comput Methods Programs Biomed; 2018 Apr; 157():85-94. PubMed ID: 29477437
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 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. Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms.
    Danala G; Patel B; Aghaei F; Heidari M; Li J; Wu T; Zheng B
    Ann Biomed Eng; 2018 Sep; 46(9):1419-1431. PubMed ID: 29748869
    [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. 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]  

  • 10. Deep Convolutional Neural Networks for breast cancer screening.
    Chougrad H; Zouaki H; Alheyane O
    Comput Methods Programs Biomed; 2018 Apr; 157():19-30. PubMed ID: 29477427
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram.
    Al-Antari MA; Al-Masni MA; Kim TS
    Adv Exp Med Biol; 2020; 1213():59-72. PubMed ID: 32030663
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network.
    Kooi T; van Ginneken B; Karssemeijer N; den Heeten A
    Med Phys; 2017 Mar; 44(3):1017-1027. PubMed ID: 28094850
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A graph-based lesion characterization and deep embedding approach for improved computer-aided diagnosis of nonmass breast MRI lesions.
    Gallego-Ortiz C; Martel AL
    Med Image Anal; 2019 Jan; 51():116-124. PubMed ID: 30412826
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. 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]  

  • 16. Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism.
    Islam W; Jones M; Faiz R; Sadeghipour N; Qiu Y; Zheng B
    Tomography; 2022 Sep; 8(5):2411-2425. PubMed ID: 36287799
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. A deep learning approach for the analysis of masses in mammograms with minimal user intervention.
    Dhungel N; Carneiro G; Bradley AP
    Med Image Anal; 2017 Apr; 37():114-128. PubMed ID: 28171807
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Computer aided detection of clusters of microcalcifications on full field digital mammograms.
    Ge J; Sahiner B; Hadjiiski LM; Chan HP; Wei J; Helvie MA; Zhou C
    Med Phys; 2006 Aug; 33(8):2975-88. PubMed ID: 16964876
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 34.