These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

134 related articles for article (PubMed ID: 37018705)

  • 1. HoVer-Trans: Anatomy-Aware HoVer-Transformer for ROI-Free Breast Cancer Diagnosis in Ultrasound Images.
    Mo Y; Han C; Liu Y; Liu M; Shi Z; Lin J; Zhao B; Huang C; Qiu B; Cui Y; Wu L; Pan X; Xu Z; Huang X; Li Z; Liu Z; Wang Y; Liang C
    IEEE Trans Med Imaging; 2023 Jun; 42(6):1696-1706. PubMed ID: 37018705
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. MsGoF: Breast lesion classification on ultrasound images by multi-scale gradational-order fusion framework.
    Zhong S; Tu C; Dong X; Feng Q; Chen W; Zhang Y
    Comput Methods Programs Biomed; 2023 Mar; 230():107346. PubMed ID: 36716637
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A VGG attention vision transformer network for benign and malignant classification of breast ultrasound images.
    Qu X; Lu H; Tang W; Wang S; Zheng D; Hou Y; Jiang J
    Med Phys; 2022 Sep; 49(9):5787-5798. PubMed ID: 35866492
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Role of inter- and extra-lesion tissue, transfer learning, and fine-tuning in the robust classification of breast lesions.
    Nastase IA; Moldovanu S; Biswas KC; Moraru L
    Sci Rep; 2024 Oct; 14(1):22754. PubMed ID: 39354128
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. 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]  

  • 8. Robust phase-based texture descriptor for classification of breast ultrasound images.
    Cai L; Wang X; Wang Y; Guo Y; Yu J; Wang Y
    Biomed Eng Online; 2015 Mar; 14():26. PubMed ID: 25889570
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. A Fusion-Based Approach for Breast Ultrasound Image Classification Using Multiple-ROI Texture and Morphological Analyses.
    Daoud MI; Bdair TM; Al-Najar M; Alazrai R
    Comput Math Methods Med; 2016; 2016():6740956. PubMed ID: 28127383
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Multi-task learning for segmentation and classification of breast tumors from ultrasound images.
    He Q; Yang Q; Su H; Wang Y
    Comput Biol Med; 2024 May; 173():108319. PubMed ID: 38513394
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. BUS-Net: Breast Tumour Detection Network for Ultrasound Images Using Bi-directional ConvLSTM and Dense Residual Connections.
    Arora R; Raman B
    J Digit Imaging; 2023 Apr; 36(2):627-646. PubMed ID: 36515746
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Diagnostic Efficiency of the Breast Ultrasound Computer-Aided Prediction Model Based on Convolutional Neural Network in Breast Cancer.
    Zhang H; Han L; Chen K; Peng Y; Lin J
    J Digit Imaging; 2020 Oct; 33(5):1218-1223. PubMed ID: 32519253
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Spatial and geometric learning for classification of breast tumors from multi-center ultrasound images: a hybrid learning approach.
    Ru J; Zhu Z; Shi J
    BMC Med Imaging; 2024 Jun; 24(1):133. PubMed ID: 38840240
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning.
    Wang Y; Choi EJ; Choi Y; Zhang H; Jin GY; Ko SB
    Ultrasound Med Biol; 2020 May; 46(5):1119-1132. PubMed ID: 32059918
    [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. Saliency map-guided hierarchical dense feature aggregation framework for breast lesion classification using ultrasound image.
    Di X; Zhong S; Zhang Y
    Comput Methods Programs Biomed; 2022 Mar; 215():106612. PubMed ID: 35033757
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Breast ultrasound image segmentation: a survey.
    Huang Q; Luo Y; Zhang Q
    Int J Comput Assist Radiol Surg; 2017 Mar; 12(3):493-507. PubMed ID: 28070777
    [TBL] [Abstract][Full Text] [Related]  

  • 20. SHA-MTL: soft and hard attention multi-task learning for automated breast cancer ultrasound image segmentation and classification.
    Zhang G; Zhao K; Hong Y; Qiu X; Zhang K; Wei B
    Int J Comput Assist Radiol Surg; 2021 Oct; 16(10):1719-1725. PubMed ID: 34254225
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.