BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

178 related articles for article (PubMed ID: 38023698)

  • 1. Deep learning-based multimodal fusion network for segmentation and classification of breast cancers using B-mode and elastography ultrasound images.
    Misra S; Yoon C; Kim KJ; Managuli R; Barr RG; Baek J; Kim C
    Bioeng Transl Med; 2023 Nov; 8(6):e10480. PubMed ID: 38023698
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Bi-Modal Transfer Learning for Classifying Breast Cancers via Combined B-Mode and Ultrasound Strain Imaging.
    Misra S; Jeon S; Managuli R; Lee S; Kim G; Yoon C; Lee S; Barr RG; Kim C
    IEEE Trans Ultrason Ferroelectr Freq Control; 2022 Jan; 69(1):222-232. PubMed ID: 34633928
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Evaluating different combination methods to analyse ultrasound and shear wave elastography images automatically through discriminative convolutional neural network in breast cancer imaging.
    Hoffmann R; Reich C; Skerl K
    Int J Comput Assist Radiol Surg; 2022 Dec; 17(12):2231-2237. PubMed ID: 36018397
    [TBL] [Abstract][Full Text] [Related]  

  • 4. CAM-QUS guided self-tuning modular CNNs with multi-loss functions for fully automated breast lesion classification in ultrasound images.
    Tasnim J; Hasan MK
    Phys Med Biol; 2023 Dec; 69(1):. PubMed ID: 38056017
    [No Abstract]   [Full Text] [Related]  

  • 5. Convolutional neural network for automated mass segmentation in mammography.
    Abdelhafiz D; Bi J; Ammar R; Yang C; Nabavi S
    BMC Bioinformatics; 2020 Dec; 21(Suppl 1):192. PubMed ID: 33297952
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Ultrasound Strain Elastography for Breast Lesions: Computer-Aided Evaluation With Quantifiable Elastographic Features.
    Xiao Y; Zeng J; Zhang X; Niu LL; Qian M; Wang CZ; Zheng HR; Zheng RQ
    J Ultrasound Med; 2017 Jun; 36(6):1089-1100. PubMed ID: 28295467
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Multimodal ultrasound fusion network for differentiating between benign and malignant solid renal tumors.
    Zhu D; Li J; Li Y; Wu J; Zhu L; Li J; Wang Z; Xu J; Dong F; Cheng J
    Front Mol Biosci; 2022; 9():982703. PubMed ID: 36148014
    [No Abstract]   [Full Text] [Related]  

  • 8. Multimodal feature learning and fusion on B-mode ultrasonography and sonoelastography using point-wise gated deep networks for prostate cancer diagnosis.
    Zhang Q; Xiong J; Cai Y; Shi J; Xu S; Zhang B
    Biomed Tech (Berl); 2020 Jan; 65(1):87-98. PubMed ID: 31743102
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Effect of a Deep Learning Framework-Based Computer-Aided Diagnosis System on the Diagnostic Performance of Radiologists in Differentiating between Malignant and Benign Masses on Breast Ultrasonography.
    Choi JS; Han BK; Ko ES; Bae JM; Ko EY; Song SH; Kwon MR; Shin JH; Hahn SY
    Korean J Radiol; 2019 May; 20(5):749-758. PubMed ID: 30993926
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Classification of multi-feature fusion ultrasound images of breast tumor within category 4 using convolutional neural networks.
    Xu P; Zhao J; Wan M; Song Q; Su Q; Wang D
    Med Phys; 2024 Mar; ():. PubMed ID: 38436433
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Multi-region radiomics for artificially intelligent diagnosis of breast cancer using multimodal ultrasound.
    Xu Z; Wang Y; Chen M; Zhang Q
    Comput Biol Med; 2022 Oct; 149():105920. PubMed ID: 35986969
    [TBL] [Abstract][Full Text] [Related]  

  • 12. B-mode ultrasound based CAD for liver cancers via multi-view privileged information learning.
    Han X; Gong B; Guo L; Wang J; Ying S; Li S; Shi J
    Neural Netw; 2023 Jul; 164():369-381. PubMed ID: 37167750
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Breast ultrasound tumor image classification using image decomposition and fusion based on adaptive multi-model spatial feature fusion.
    Zhuang Z; Yang Z; Raj ANJ; Wei C; Jin P; Zhuang S
    Comput Methods Programs Biomed; 2021 Sep; 208():106221. PubMed ID: 34144251
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Breast lesion classification based on supersonic shear-wave elastography and automated lesion segmentation from B-mode ultrasound images.
    Yu Y; Xiao Y; Cheng J; Chiu B
    Comput Biol Med; 2018 Feb; 93():31-46. PubMed ID: 29275098
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks.
    Moon WK; Lee YW; Ke HH; Lee SH; Huang CS; Chang RF
    Comput Methods Programs Biomed; 2020 Jul; 190():105361. PubMed ID: 32007839
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images.
    Ma H; Tian R; Li H; Sun H; Lu G; Liu R; Wang Z
    Biomed Eng Online; 2021 Nov; 20(1):112. PubMed ID: 34794443
    [TBL] [Abstract][Full Text] [Related]  

  • 18. An effective convolutional neural network for classification of benign and malignant breast and thyroid tumors from ultrasound images.
    Tian R; Yu M; Liao L; Zhang C; Zhao J; Sang L; Qian W; Wang Z; Huang L; Ma H
    Phys Eng Sci Med; 2023 Sep; 46(3):995-1013. PubMed ID: 37195403
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning.
    Ryu H; Shin SY; Lee JY; Lee KM; Kang HJ; Yi J
    Eur Radiol; 2021 Nov; 31(11):8733-8742. PubMed ID: 33881566
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Breast ultrasound image segmentation: A coarse-to-fine fusion convolutional neural network.
    Wang K; Liang S; Zhong S; Feng Q; Ning Z; Zhang Y
    Med Phys; 2021 Aug; 48(8):4262-4278. PubMed ID: 34053092
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.