BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

186 related articles for article (PubMed ID: 33493108)

  • 1. 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]  

  • 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. 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]  

  • 4. 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]  

  • 5. Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model.
    Tan M; Pu J; Zheng B
    Int J Comput Assist Radiol Surg; 2014 Nov; 9(6):1005-20. PubMed ID: 24664267
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 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 Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods.
    Danala G; Maryada SK; Islam W; Faiz R; Jones M; Qiu Y; Zheng B
    Bioengineering (Basel); 2022 Jun; 9(6):. PubMed ID: 35735499
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images.
    Mirniaharikandehei S; Heidari M; Danala G; Lakshmivarahan S; Zheng B
    Comput Methods Programs Biomed; 2021 Mar; 200():105937. PubMed ID: 33486339
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Improving mammography lesion classification by optimal fusion of handcrafted and deep transfer learning features.
    Jones MA; Faiz R; Qiu Y; Zheng B
    Phys Med Biol; 2022 Feb; 67(5):. PubMed ID: 35130517
    [No Abstract]   [Full Text] [Related]  

  • 11. 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]  

  • 12. 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]  

  • 13. 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]  

  • 14. Applying Quantitative Radiographic Image Markers to Predict Clinical Complications After Aneurysmal Subarachnoid Hemorrhage: A Pilot Study.
    Danala G; Desai M; Ray B; Heidari M; Maryada SKR; Prodan CI; Zheng B
    Ann Biomed Eng; 2022 Apr; 50(4):413-425. PubMed ID: 35112157
    [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. Computer-aided diagnosis of contrast-enhanced spectral mammography: A feasibility study.
    Patel BK; Ranjbar S; Wu T; Pockaj BA; Li J; Zhang N; Lobbes M; Zhang B; Mitchell JR
    Eur J Radiol; 2018 Jan; 98():207-213. PubMed ID: 29279165
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Improving performance of computer-aided detection scheme by combining results from two machine learning classifiers.
    Park SC; Pu J; Zheng B
    Acad Radiol; 2009 Mar; 16(3):266-74. PubMed ID: 19201355
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Characterization of masses in digital breast tomosynthesis: comparison of machine learning in projection views and reconstructed slices.
    Chan HP; Wu YT; Sahiner B; Wei J; Helvie MA; Zhang Y; Moore RH; Kopans DB; Hadjiiski L; Way T
    Med Phys; 2010 Jul; 37(7):3576-86. PubMed ID: 20831065
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Applying a radiomics-based CAD scheme to classify between malignant and benign pancreatic tumors using CT images.
    Gai T; Thai T; Jones M; Jo J; Zheng B
    J Xray Sci Technol; 2022; 30(2):377-388. PubMed ID: 35095015
    [TBL] [Abstract][Full Text] [Related]  

  • 20. An interactive system for computer-aided diagnosis of breast masses.
    Wang X; Li L; Liu W; Xu W; Lederman D; Zheng B
    J Digit Imaging; 2012 Oct; 25(5):570-9. PubMed ID: 22234836
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.