BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

534 related articles for article (PubMed ID: 32554139)

  • 21. Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network.
    Rahman H; Naik Bukht TF; Ahmad R; Almadhor A; Javed AR
    Comput Intell Neurosci; 2023; 2023():7717712. PubMed ID: 36909966
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Deep Learning to Improve Breast Cancer Detection on Screening Mammography.
    Shen L; Margolies LR; Rothstein JH; Fluder E; McBride R; Sieh W
    Sci Rep; 2019 Aug; 9(1):12495. PubMed ID: 31467326
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Automatic mass detection in mammograms using deep convolutional neural networks.
    Agarwal R; Diaz O; Lladó X; Yap MH; Martí R
    J Med Imaging (Bellingham); 2019 Jul; 6(3):031409. PubMed ID: 35834317
    [TBL] [Abstract][Full Text] [Related]  

  • 24. 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]  

  • 25. 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]  

  • 26. ETECADx: Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-ray Breast Images.
    Al-Hejri AM; Al-Tam RM; Fazea M; Sable AH; Lee S; Al-Antari MA
    Diagnostics (Basel); 2022 Dec; 13(1):. PubMed ID: 36611382
    [TBL] [Abstract][Full Text] [Related]  

  • 27. A Novel Algorithm for Breast Mass Classification in Digital Mammography Based on Feature Fusion.
    Zhang Q; Li Y; Zhao G; Man P; Lin Y; Wang M
    J Healthc Eng; 2020; 2020():8860011. PubMed ID: 33425311
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography.
    Mendel K; Li H; Sheth D; Giger M
    Acad Radiol; 2019 Jun; 26(6):735-743. PubMed ID: 30076083
    [TBL] [Abstract][Full Text] [Related]  

  • 29. 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]  

  • 30. Deep convolutional neural networks for mammography: advances, challenges and applications.
    Abdelhafiz D; Yang C; Ammar R; Nabavi S
    BMC Bioinformatics; 2019 Jun; 20(Suppl 11):281. PubMed ID: 31167642
    [TBL] [Abstract][Full Text] [Related]  

  • 31. A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-ray Mammograms.
    Al-Tam RM; Al-Hejri AM; Narangale SM; Samee NA; Mahmoud NF; Al-Masni MA; Al-Antari MA
    Biomedicines; 2022 Nov; 10(11):. PubMed ID: 36428538
    [TBL] [Abstract][Full Text] [Related]  

  • 32. A multi-context CNN ensemble for small lesion detection.
    Savelli B; Bria A; Molinara M; Marrocco C; Tortorella F
    Artif Intell Med; 2020 Mar; 103():101749. PubMed ID: 32143786
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism.
    Islam W; Jones M; Faiz R; Sadeghipour N; Qiu Y; Zheng B
    Tomography; 2022 Sep; 8(5):2411-2425. PubMed ID: 36287799
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning.
    Aljuaid H; Alturki N; Alsubaie N; Cavallaro L; Liotta A
    Comput Methods Programs Biomed; 2022 Aug; 223():106951. PubMed ID: 35767911
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Automatic Breast Mass Segmentation and Classification Using Subtraction of Temporally Sequential Digital Mammograms.
    Loizidou K; Skouroumouni G; Nikolaou C; Pitris C
    IEEE J Transl Eng Health Med; 2022; 10():1801111. PubMed ID: 36519002
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Deep learning for mass detection in Full Field Digital Mammograms.
    Agarwal R; Díaz O; Yap MH; Lladó X; Martí R
    Comput Biol Med; 2020 Jun; 121():103774. PubMed ID: 32339095
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning.
    Zeiser FA; da Costa CA; Zonta T; Marques NMC; Roehe AV; Moreno M; da Rosa Righi R
    J Digit Imaging; 2020 Aug; 33(4):858-868. PubMed ID: 32206943
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Deep Learning Capabilities for the Categorization of Microcalcification.
    Kumar Singh K; Kumar S; Antonakakis M; Moirogiorgou K; Deep A; Kashyap KL; Bajpai MK; Zervakis M
    Int J Environ Res Public Health; 2022 Feb; 19(4):. PubMed ID: 35206347
    [TBL] [Abstract][Full Text] [Related]  

  • 39. 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]  

  • 40. Review on Computer Aided Breast Cancer Detection and Diagnosis using Machine Learning Methods on Mammogram Image.
    Kuttan GO; Elayidom MS
    Curr Med Imaging; 2023; 19(12):1361-1371. PubMed ID: 36788681
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 27.