BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

140 related articles for article (PubMed ID: 34460371)

  • 1. Identifying Women With Mammographically- Occult Breast Cancer Leveraging GAN-Simulated Mammograms.
    Lee J; Nishikawa RM
    IEEE Trans Med Imaging; 2022 Jan; 41(1):225-236. PubMed ID: 34460371
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Impact of GAN artifacts for simulating mammograms on identifying mammographically occult cancer.
    Lee J; Mustafaev T; Nishikawa RM
    J Med Imaging (Bellingham); 2023 Sep; 10(5):054503. PubMed ID: 37840849
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Detecting mammographically occult cancer in women with dense breasts using deep convolutional neural network and Radon Cumulative Distribution Transform.
    Lee J; Nishikawa RM
    J Med Imaging (Bellingham); 2019 Oct; 6(4):044502. PubMed ID: 31890746
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Improving lesion detection in mammograms by leveraging a Cycle-GAN-based lesion remover.
    Lee J; Nishikawa RM
    Breast Cancer Res; 2024 Feb; 26(1):21. PubMed ID: 38303004
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Breast cancer detection using synthetic mammograms from generative adversarial networks in convolutional neural networks.
    Guan S; Loew M
    J Med Imaging (Bellingham); 2019 Jul; 6(3):031411. PubMed ID: 30915386
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Role of General Adversarial Networks in Mammogram Analysis: A Review.
    Gopal A; Gandhimaruthian L; Ali J
    Curr Med Imaging; 2020; 16(7):863-877. PubMed ID: 33059556
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Unsupervised anomaly detection with generative adversarial networks in mammography.
    Park S; Lee KH; Ko B; Kim N
    Sci Rep; 2023 Feb; 13(1):2925. PubMed ID: 36805637
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Performance of DWI as a Rapid Unenhanced Technique for Detecting Mammographically Occult Breast Cancer in Elevated-Risk Women With Dense Breasts.
    McDonald ES; Hammersley JA; Chou SH; Rahbar H; Scheel JR; Lee CI; Liu CL; Lehman CD; Partridge SC
    AJR Am J Roentgenol; 2016 Jul; 207(1):205-16. PubMed ID: 27077731
    [TBL] [Abstract][Full Text] [Related]  

  • 9. RAMS: Remote and automatic mammogram screening.
    Cogan T; Cogan M; Tamil L
    Comput Biol Med; 2019 Apr; 107():18-29. PubMed ID: 30771549
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. Using Convolutional Neural Network with Cheat Sheet and Data Augmentation to Detect Breast Cancer in Mammograms.
    Ramadan SZ
    Comput Math Methods Med; 2020; 2020():9523404. PubMed ID: 33193807
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Biennial versus annual mammography and the risk of late-stage breast cancer.
    White E; Miglioretti DL; Yankaskas BC; Geller BM; Rosenberg RD; Kerlikowske K; Saba L; Vacek PM; Carney PA; Buist DS; Oestreicher N; Barlow W; Ballard-Barbash R; Taplin SH
    J Natl Cancer Inst; 2004 Dec; 96(24):1832-9. PubMed ID: 15601639
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Breast-Specific γ-Imaging for the Detection of Mammographically Occult Breast Cancer in Women at Increased Risk.
    Brem RF; Ruda RC; Yang JL; Coffey CM; Rapelyea JA
    J Nucl Med; 2016 May; 57(5):678-84. PubMed ID: 26823569
    [TBL] [Abstract][Full Text] [Related]  

  • 14. SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis.
    Gao F; Wu T; Li J; Zheng B; Ruan L; Shang D; Patel B
    Comput Med Imaging Graph; 2018 Dec; 70():53-62. PubMed ID: 30292910
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Fully Automated Breast Density Segmentation and Classification Using Deep Learning.
    Saffari N; Rashwan HA; Abdel-Nasser M; Kumar Singh V; Arenas M; Mangina E; Herrera B; Puig D
    Diagnostics (Basel); 2020 Nov; 10(11):. PubMed ID: 33238512
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A deep learning method for classifying mammographic breast density categories.
    Mohamed AA; Berg WA; Peng H; Luo Y; Jankowitz RC; Wu S
    Med Phys; 2018 Jan; 45(1):314-321. PubMed ID: 29159811
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography.
    Tsai KJ; Chou MC; Li HM; Liu ST; Hsu JH; Yeh WC; Hung CM; Yeh CY; Hwang SH
    Sensors (Basel); 2022 Feb; 22(3):. PubMed ID: 35161903
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Multi-scale attention-based convolutional neural network for classification of breast masses in mammograms.
    Niu J; Li H; Zhang C; Li D
    Med Phys; 2021 Jul; 48(7):3878-3892. PubMed ID: 33982807
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Feature fusion Siamese network for breast cancer detection comparing current and prior mammograms.
    Bai J; Jin A; Wang T; Yang C; Nabavi S
    Med Phys; 2022 Jun; 49(6):3654-3669. PubMed ID: 35271746
    [TBL] [Abstract][Full Text] [Related]  

  • 20.
    ; ; . PubMed ID:
    [No Abstract]   [Full Text] [Related]  

    [Next]    [New Search]
    of 7.