BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

918 related articles for article (PubMed ID: 31306148)

  • 1. 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]  

  • 2. Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks.
    Bandeira Diniz JO; Bandeira Diniz PH; Azevedo Valente TL; Corrêa Silva A; de Paiva AC; Gattass M
    Comput Methods Programs Biomed; 2018 Mar; 156():191-207. PubMed ID: 29428071
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. DAU-Net: Dual attention-aided U-Net for segmenting tumor in breast ultrasound images.
    Pramanik P; Roy A; Cuevas E; Perez-Cisneros M; Sarkar R
    PLoS One; 2024; 19(5):e0303670. PubMed ID: 38820462
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Channel Attention Module With Multiscale Grid Average Pooling for Breast Cancer Segmentation in an Ultrasound Image.
    Lee H; Park J; Hwang JY
    IEEE Trans Ultrason Ferroelectr Freq Control; 2020 Jul; 67(7):1344-1353. PubMed ID: 32054578
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Multi-planar 3D breast segmentation in MRI via deep convolutional neural networks.
    Piantadosi G; Sansone M; Fusco R; Sansone C
    Artif Intell Med; 2020 Mar; 103():101781. PubMed ID: 32143788
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. 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]  

  • 10. Automated and real-time segmentation of suspicious breast masses using convolutional neural network.
    Kumar V; Webb JM; Gregory A; Denis M; Meixner DD; Bayat M; Whaley DH; Fatemi M; Alizad A
    PLoS One; 2018; 13(5):e0195816. PubMed ID: 29768415
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. 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]  

  • 13. Fully multi-target segmentation for breast ultrasound image based on fully convolutional network.
    Zhang Y; Liu Y; Cheng H; Li Z; Liu C
    Med Biol Eng Comput; 2020 Sep; 58(9):2049-2061. PubMed ID: 32638276
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning.
    Yousefi M; Krzyżak A; Suen CY
    Comput Biol Med; 2018 May; 96():283-293. PubMed ID: 29665537
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Computer-aided tumor detection in automated breast ultrasound using a 3-D convolutional neural network.
    Moon WK; Huang YS; Hsu CH; Chang Chien TY; Chang JM; Lee SH; Huang CS; Chang RF
    Comput Methods Programs Biomed; 2020 Jul; 190():105360. PubMed ID: 32007838
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Mass Segmentation in Automated 3-D Breast Ultrasound Using Adaptive Region Growing and Supervised Edge-Based Deformable Model.
    Kozegar E; Soryani M; Behnam H; Salamati M; Tan T
    IEEE Trans Med Imaging; 2018 Apr; 37(4):918-928. PubMed ID: 29610071
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Two-stage ultrasound image segmentation using U-Net and test time augmentation.
    Amiri M; Brooks R; Behboodi B; Rivaz H
    Int J Comput Assist Radiol Surg; 2020 Jun; 15(6):981-988. PubMed ID: 32350786
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. 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]  

  • 20. Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging.
    Valvano G; Santini G; Martini N; Ripoli A; Iacconi C; Chiappino D; Della Latta D
    J Healthc Eng; 2019; 2019():9360941. PubMed ID: 31093321
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 46.