BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

715 related articles for article (PubMed ID: 32155611)

  • 1. Automated fibroglandular tissue segmentation in breast MRI using generative adversarial networks.
    Ma X; Wang J; Zheng X; Liu Z; Long W; Zhang Y; Wei J; Lu Y
    Phys Med Biol; 2020 May; 65(10):105006. PubMed ID: 32155611
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Using deep learning to segment breast and fibroglandular tissue in MRI volumes.
    Dalmış MU; Litjens G; Holland K; Setio A; Mann R; Karssemeijer N; Gubern-Mérida A
    Med Phys; 2017 Feb; 44(2):533-546. PubMed ID: 28035663
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Fully Automated Convolutional Neural Network Method for Quantification of Breast MRI Fibroglandular Tissue and Background Parenchymal Enhancement.
    Ha R; Chang P; Mema E; Mutasa S; Karcich J; Wynn RT; Liu MZ; Jambawalikar S
    J Digit Imaging; 2019 Feb; 32(1):141-147. PubMed ID: 30076489
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net.
    Zhang Y; Chen JH; Chang KT; Park VY; Kim MJ; Chan S; Chang P; Chow D; Luk A; Kwong T; Su MY
    Acad Radiol; 2019 Nov; 26(11):1526-1535. PubMed ID: 30713130
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Fully automatic quantification of fibroglandular tissue and background parenchymal enhancement with accurate implementation for axial and sagittal breast MRI protocols.
    Wei D; Jahani N; Cohen E; Weinstein S; Hsieh MK; Pantalone L; Kontos D
    Med Phys; 2021 Jan; 48(1):238-252. PubMed ID: 33150617
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Fully Automatic Assessment of Background Parenchymal Enhancement on Breast MRI Using Machine-Learning Models.
    Nam Y; Park GE; Kang J; Kim SH
    J Magn Reson Imaging; 2021 Mar; 53(3):818-826. PubMed ID: 33219624
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method.
    Wu S; Weinstein SP; Conant EF; Kontos D
    Med Phys; 2013 Dec; 40(12):122302. PubMed ID: 24320533
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Generalizable attention U-Net for segmentation of fibroglandular tissue and background parenchymal enhancement in breast DCE-MRI.
    Nowakowska S; Borkowski K; Ruppert CM; Landsmann A; Marcon M; Berger N; Boss A; Ciritsis A; Rossi C
    Insights Imaging; 2023 Nov; 14(1):185. PubMed ID: 37932462
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Development of U-Net Breast Density Segmentation Method for Fat-Sat MR Images Using Transfer Learning Based on Non-Fat-Sat Model.
    Zhang Y; Chan S; Chen JH; Chang KT; Lin CY; Pan HB; Lin WC; Kwong T; Parajuli R; Mehta RS; Chien SH; Su MY
    J Digit Imaging; 2021 Aug; 34(4):877-887. PubMed ID: 34244879
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Amount of fibroglandular tissue FGT and background parenchymal enhancement BPE in relation to breast cancer risk and false positives in a breast MRI screening program : A retrospective cohort study.
    Vreemann S; Dalmis MU; Bult P; Karssemeijer N; Broeders MJM; Gubern-Mérida A; Mann RM
    Eur Radiol; 2019 Sep; 29(9):4678-4690. PubMed ID: 30796568
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Visual ensemble selection of deep convolutional neural networks for 3D segmentation of breast tumors on dynamic contrast enhanced MRI.
    Rahimpour M; Saint Martin MJ; Frouin F; Akl P; Orlhac F; Koole M; Malhaire C
    Eur Radiol; 2023 Feb; 33(2):959-969. PubMed ID: 36074262
    [TBL] [Abstract][Full Text] [Related]  

  • 12. An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U-nets.
    Fashandi H; Kuling G; Lu Y; Wu H; Martel AL
    Med Phys; 2019 Mar; 46(3):1230-1244. PubMed ID: 30609062
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Segmentation of whole breast and fibroglandular tissue using nnU-Net in dynamic contrast enhanced MR images.
    Huo L; Hu X; Xiao Q; Gu Y; Chu X; Jiang L
    Magn Reson Imaging; 2021 Oct; 82():31-41. PubMed ID: 34147598
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI.
    Jiao H; Jiang X; Pang Z; Lin X; Huang Y; Li L
    Comput Math Methods Med; 2020; 2020():2413706. PubMed ID: 32454879
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Clinical applicability and relevance of fibroglandular tissue segmentation on routine T1 weighted breast MRI.
    Pujara AC; Mikheev A; Rusinek H; Rallapalli H; Walczyk J; Gao Y; Chhor C; Pysarenko K; Babb JS; Melsaether AN
    Clin Imaging; 2017; 42():119-125. PubMed ID: 27951458
    [TBL] [Abstract][Full Text] [Related]  

  • 16. U-Net based deep learning bladder segmentation in CT urography.
    Ma X; Hadjiiski LM; Wei J; Chan HP; Cha KH; Cohan RH; Caoili EM; Samala R; Zhou C; Lu Y
    Med Phys; 2019 Apr; 46(4):1752-1765. PubMed ID: 30734932
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Quantitative Assessment of Breast Parenchymal Uptake on 18F-FDG PET/CT: Correlation with Age, Background Parenchymal Enhancement, and Amount of Fibroglandular Tissue on MRI.
    Leithner D; Baltzer PA; Magometschnigg HF; Wengert GJ; Karanikas G; Helbich TH; Weber M; Wadsak W; Pinker K
    J Nucl Med; 2016 Oct; 57(10):1518-1522. PubMed ID: 27230924
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Automated pectoral muscle identification on MLO-view mammograms: Comparison of deep neural network to conventional computer vision.
    Ma X; Wei J; Zhou C; Helvie MA; Chan HP; Hadjiiski LM; Lu Y
    Med Phys; 2019 May; 46(5):2103-2114. PubMed ID: 30771257
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Fully automated segmentation of whole breast using dynamic programming in dynamic contrast enhanced MR images.
    Jiang L; Hu X; Xiao Q; Gu Y; Li Q
    Med Phys; 2017 Jun; 44(6):2400-2414. PubMed ID: 28375584
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Preliminary analysis: Background parenchymal 18F-FDG uptake in breast cancer patients appears to correlate with background parenchymal enhancement and to vary by distance from the index cancer.
    Kim E; Mema E; Axelrod D; Sigmund E; Kim SG; Babb J; Melsaether AN
    Eur J Radiol; 2019 Jan; 110():163-168. PubMed ID: 30599855
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 36.