These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

138 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. 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]  

  • 16. 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]  

  • 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 7.