BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

1466 related articles for article (PubMed ID: 31262255)

  • 1. An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures.
    Cao Z; Duan L; Yang G; Yue T; Chen Q
    BMC Med Imaging; 2019 Jul; 19(1):51. PubMed ID: 31262255
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 4. New one-step model of breast tumor locating based on deep learning.
    Tao C; Chen K; Han L; Peng Y; Li C; Hua Z; Lin J
    J Xray Sci Technol; 2019; 27(5):839-856. PubMed ID: 31306148
    [TBL] [Abstract][Full Text] [Related]  

  • 5. BIRADS features-oriented semi-supervised deep learning for breast ultrasound computer-aided diagnosis.
    Zhang E; Seiler S; Chen M; Lu W; Gu X
    Phys Med Biol; 2020 Jun; 65(12):125005. PubMed ID: 32155605
    [TBL] [Abstract][Full Text] [Related]  

  • 6. An Edge-Based Selection Method for Improving Regions-of-Interest Localizations Obtained Using Multiple Deep Learning Object-Detection Models in Breast Ultrasound Images.
    Daoud MI; Al-Ali A; Alazrai R; Al-Najar MS; Alsaify BA; Ali MZ; Alouneh S
    Sensors (Basel); 2022 Sep; 22(18):. PubMed ID: 36146070
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms.
    Al-Antari MA; Han SM; Kim TS
    Comput Methods Programs Biomed; 2020 Nov; 196():105584. PubMed ID: 32554139
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Deep Convolutional Neural Networks for breast cancer screening.
    Chougrad H; Zouaki H; Alheyane O
    Comput Methods Programs Biomed; 2018 Apr; 157():19-30. PubMed ID: 29477427
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A deep learning- and partial least square regression-based model observer for a low-contrast lesion detection task in CT.
    Gong H; Yu L; Leng S; Dilger SK; Ren L; Zhou W; Fletcher JG; McCollough CH
    Med Phys; 2019 May; 46(5):2052-2063. PubMed ID: 30889282
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Breast Tumor Classification in Ultrasound Images Using Combined Deep and Handcrafted Features.
    Daoud MI; Abdel-Rahman S; Bdair TM; Al-Najar MS; Al-Hawari FH; Alazrai R
    Sensors (Basel); 2020 Nov; 20(23):. PubMed ID: 33265900
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Computer-aided diagnosis system for breast ultrasound images using deep learning.
    Tanaka H; Chiu SW; Watanabe T; Kaoku S; Yamaguchi T
    Phys Med Biol; 2019 Dec; 64(23):235013. PubMed ID: 31645021
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Skin lesion classification with ensembles of deep convolutional neural networks.
    Harangi B
    J Biomed Inform; 2018 Oct; 86():25-32. PubMed ID: 30103029
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images.
    Brehar R; Mitrea DA; Vancea F; Marita T; Nedevschi S; Lupsor-Platon M; Rotaru M; Badea RI
    Sensors (Basel); 2020 May; 20(11):. PubMed ID: 32485986
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Boundary-aware Semi-supervised Deep Learning for Breast Ultrasound Computer-Aided Diagnosis.
    Zhang E; Seiler S; Chen M; Lu W; Gu X
    Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():947-950. PubMed ID: 31946050
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Deep Learning Networks for Breast Lesion Classification in Ultrasound Images: A Comparative Study.
    Ferreira MR; Torres HR; Oliveira B; de Araujo ARVF; Morais P; Novais P; Vilaca JL
    Annu Int Conf IEEE Eng Med Biol Soc; 2023 Jul; 2023():1-4. PubMed ID: 38083151
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography.
    Samala RK; Chan HP; Hadjiiski L; Helvie MA; Wei J; Cha K
    Med Phys; 2016 Dec; 43(12):6654. PubMed ID: 27908154
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks.
    Yap MH; Pons G; Marti J; Ganau S; Sentis M; Zwiggelaar R; Davison AK; Marti R; Moi Hoon Yap ; Pons G; Marti J; Ganau S; Sentis M; Zwiggelaar R; Davison AK; Marti R
    IEEE J Biomed Health Inform; 2018 Jul; 22(4):1218-1226. PubMed ID: 28796627
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 74.