BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

143 related articles for article (PubMed ID: 33882475)

  • 1. Mass segmentation for whole mammograms via attentive multi-task learning framework.
    Hou X; Bai Y; Xie Y; Li Y
    Phys Med Biol; 2021 May; 66(10):. PubMed ID: 33882475
    [TBL] [Abstract][Full Text] [Related]  

  • 2. AUNet: attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms.
    Sun H; Li C; Liu B; Liu Z; Wang M; Zheng H; Dagan Feng D; Wang S
    Phys Med Biol; 2020 Feb; 65(5):055005. PubMed ID: 31722327
    [TBL] [Abstract][Full Text] [Related]  

  • 3. TrEnD: A transformer-based encoder-decoder model with adaptive patch embedding for mass segmentation in mammograms.
    Liu D; Wu B; Li C; Sun Z; Zhang N
    Med Phys; 2023 May; 50(5):2884-2899. PubMed ID: 36609788
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A multi-task fusion model based on a residual-Multi-layer perceptron network for mammographic breast cancer screening.
    Zhong Y; Piao Y; Tan B; Liu J
    Comput Methods Programs Biomed; 2024 Apr; 247():108101. PubMed ID: 38432087
    [TBL] [Abstract][Full Text] [Related]  

  • 5. YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms.
    Su Y; Liu Q; Xie W; Hu P
    Comput Methods Programs Biomed; 2022 Jun; 221():106903. PubMed ID: 35636358
    [TBL] [Abstract][Full Text] [Related]  

  • 6. FS-UNet: Mass segmentation in mammograms using an encoder-decoder architecture with feature strengthening.
    Pi J; Qi Y; Lou M; Li X; Wang Y; Xu C; Ma Y
    Comput Biol Med; 2021 Oct; 137():104800. PubMed ID: 34507155
    [TBL] [Abstract][Full Text] [Related]  

  • 7. SAP-cGAN: Adversarial learning for breast mass segmentation in digital mammogram based on superpixel average pooling.
    Li Y; Zhao G; Zhang Q; Lin Y; Wang M
    Med Phys; 2021 Mar; 48(3):1157-1167. PubMed ID: 33340125
    [TBL] [Abstract][Full Text] [Related]  

  • 8. DCANet: Dual contextual affinity network for mass segmentation in whole mammograms.
    Lou M; Qi Y; Meng J; Xu C; Wang Y; Pi J; Ma Y
    Med Phys; 2021 Aug; 48(8):4291-4303. PubMed ID: 34061371
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. 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]  

  • 11. Automated mammographic mass detection using deformable convolution and multiscale features.
    Peng J; Bao C; Hu C; Wang X; Jian W; Liu W
    Med Biol Eng Comput; 2020 Jul; 58(7):1405-1417. PubMed ID: 32297129
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Multicontext multitask learning networks for mass detection in mammogram.
    Shen R; Zhou K; Yan K; Tian K; Zhang J
    Med Phys; 2020 Apr; 47(4):1566-1578. PubMed ID: 31799718
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Whole mammographic mass segmentation using attention mechanism and multiscale pooling adversarial network.
    Wang Y; Wang S; Chen J; Wu C
    J Med Imaging (Bellingham); 2020 Sep; 7(5):054503. PubMed ID: 33102621
    [No Abstract]   [Full Text] [Related]  

  • 14. Integrating segmentation information into CNN for breast cancer diagnosis of mammographic masses.
    Tsochatzidis L; Koutla P; Costaridou L; Pratikakis I
    Comput Methods Programs Biomed; 2021 Mar; 200():105913. PubMed ID: 33422854
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Generating Full-Field Digital Mammogram From Digitized Screen-Film Mammogram for Breast Cancer Screening With High-Resolution Generative Adversarial Network.
    Zhou Y; Wei J; Wu D; Zhang Y
    Front Oncol; 2022; 12():868257. PubMed ID: 35574397
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Mammogram mass segmentation and classification based on cross-view VAE and spatial hidden factor disentanglement.
    Ma Y; Peng Y
    Phys Eng Sci Med; 2024 Mar; 47(1):223-238. PubMed ID: 38150059
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Comparison of segmentation-free and segmentation-dependent computer-aided diagnosis of breast masses on a public mammography dataset.
    Sawyer Lee R; Dunnmon JA; He A; Tang S; RĂ© C; Rubin DL
    J Biomed Inform; 2021 Jan; 113():103656. PubMed ID: 33309994
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Breast cancer detection and classification in mammogram using a three-stage deep learning framework based on PAA algorithm.
    Jiang J; Peng J; Hu C; Jian W; Wang X; Liu W
    Artif Intell Med; 2022 Dec; 134():102419. PubMed ID: 36462904
    [TBL] [Abstract][Full Text] [Related]  

  • 19. FSE-Net: feature selection and enhancement network for mammogram classification.
    Liao C; Wen X; Qi S; Liu Y; Cao R
    Phys Med Biol; 2023 Sep; 68(19):. PubMed ID: 37712226
    [No Abstract]   [Full Text] [Related]  

  • 20. Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography.
    Li H; Chen D; Nailon WH; Davies ME; Laurenson DI
    IEEE Trans Med Imaging; 2022 Jan; 41(1):3-13. PubMed ID: 34351855
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.