BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

157 related articles for article (PubMed ID: 37253896)

  • 1. Weakly Supervised Breast Lesion Detection in Dynamic Contrast-Enhanced MRI.
    Sun R; Wei C; Jiang Z; Huang G; Xie Y; Nie S
    J Digit Imaging; 2023 Aug; 36(4):1553-1564. PubMed ID: 37253896
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Weakly supervised breast lesion detection in DCE-MRI using self-transfer learning.
    Sun R; Zhang X; Xie Y; Nie S
    Med Phys; 2023 Aug; 50(8):4960-4972. PubMed ID: 36820793
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images.
    Zhou J; Luo LY; Dou Q; Chen H; Chen C; Li GJ; Jiang ZF; Heng PA
    J Magn Reson Imaging; 2019 Oct; 50(4):1144-1151. PubMed ID: 30924997
    [TBL] [Abstract][Full Text] [Related]  

  • 4. MRI-Based Breast Cancer Classification and Localization by Multiparametric Feature Extraction and Combination Using Deep Learning.
    Cong C; Li X; Zhang C; Zhang J; Sun K; Liu L; Ambale-Venkatesh B; Chen X; Wang Y
    J Magn Reson Imaging; 2024 Jan; 59(1):148-161. PubMed ID: 37013422
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Automatic deep learning method for detection and classification of breast lesions in dynamic contrast-enhanced magnetic resonance imaging.
    Gao W; Chen J; Zhang B; Wei X; Zhong J; Li X; He X; Zhao F; Chen X
    Quant Imaging Med Surg; 2023 Apr; 13(4):2620-2633. PubMed ID: 37064362
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Weakly-supervised deep learning for ultrasound diagnosis of breast cancer.
    Kim J; Kim HJ; Kim C; Lee JH; Kim KW; Park YM; Kim HW; Ki SY; Kim YM; Kim WH
    Sci Rep; 2021 Dec; 11(1):24382. PubMed ID: 34934144
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Assessing the performance of benign and malignant breast lesion classification with bilateral TIC differentiation and other effective features in DCE-MRI.
    Li H; Sun H; Liu S; Zhang W; Arukalam FM; Ma H; Qian W
    J Magn Reson Imaging; 2019 Aug; 50(2):465-473. PubMed ID: 30688398
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Radiomics Based on Multimodal MRI for the Differential Diagnosis of Benign and Malignant Breast Lesions.
    Zhang Q; Peng Y; Liu W; Bai J; Zheng J; Yang X; Zhou L
    J Magn Reson Imaging; 2020 Aug; 52(2):596-607. PubMed ID: 32061014
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Textural analysis of early-phase spatiotemporal changes in contrast enhancement of breast lesions imaged with an ultrafast DCE-MRI protocol.
    Milenković J; Dalmış MU; Žgajnar J; Platel B
    Med Phys; 2017 Sep; 44(9):4652-4664. PubMed ID: 28622412
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Transfer Learning Strategy Based on Unsupervised Learning and Ensemble Learning for Breast Cancer Molecular Subtype Prediction Using Dynamic Contrast-Enhanced MRI.
    Sun R; Hou X; Li X; Xie Y; Nie S
    J Magn Reson Imaging; 2022 May; 55(5):1518-1534. PubMed ID: 34668601
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A computerized global MR image feature analysis scheme to assist diagnosis of breast cancer: a preliminary assessment.
    Yang Q; Li L; Zhang J; Shao G; Zheng B
    Eur J Radiol; 2014 Jul; 83(7):1086-1091. PubMed ID: 24743001
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Total variation based DCE-MRI decomposition by separating lesion from background for time-intensity curve estimation.
    Liu H; Zheng Y; Liang D; Tang P; Ren F; Zhang L; Zhao Z
    Med Phys; 2017 Jun; 44(6):2321-2331. PubMed ID: 28370063
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Weakly Supervised Deep Learning Approach to Breast MRI Assessment.
    Liu MZ; Swintelski C; Sun S; Siddique M; Desperito E; Jambawalikar S; Ha R
    Acad Radiol; 2022 Jan; 29 Suppl 1():S166-S172. PubMed ID: 34108114
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Characterization of spatiotemporal changes for the classification of dynamic contrast-enhanced magnetic-resonance breast lesions.
    Milenković J; Hertl K; Košir A; Zibert J; Tasič JF
    Artif Intell Med; 2013 Jun; 58(2):101-14. PubMed ID: 23548472
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI.
    Chen W; Giger ML; Bick U; Newstead GM
    Med Phys; 2006 Aug; 33(8):2878-87. PubMed ID: 16964864
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Combined use of T2-weighted MRI and T1-weighted dynamic contrast-enhanced MRI in the automated analysis of breast lesions.
    Bhooshan N; Giger M; Lan L; Li H; Marquez A; Shimauchi A; Newstead GM
    Magn Reson Med; 2011 Aug; 66(2):555-64. PubMed ID: 21523818
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Lesion attention guided neural network for contrast-enhanced mammography-based biomarker status prediction in breast cancer.
    Qian N; Jiang W; Wu X; Zhang N; Yu H; Guo Y
    Comput Methods Programs Biomed; 2024 Jun; 250():108194. PubMed ID: 38678959
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Weakly Supervised MRI Slice-Level Deep Learning Classification of Prostate Cancer Approximates Full Voxel- and Slice-Level Annotation: Effect of Increasing Training Set Size.
    Weißer C; Netzer N; Görtz M; Schütz V; Hielscher T; Schwab C; Hohenfellner M; Schlemmer HP; Maier-Hein KH; Bonekamp D
    J Magn Reson Imaging; 2024 Apr; 59(4):1409-1422. PubMed ID: 37504495
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A weakly supervised NMF method to decipher molecular subtype-related dynamic patterns in breast DCE-MR images.
    Guan J; Fan M; Li L
    Phys Med Biol; 2023 Oct; 68(21):. PubMed ID: 37757849
    [No Abstract]   [Full Text] [Related]  

  • 20. Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution.
    Ji Y; Li H; Edwards AV; Papaioannou J; Ma W; Liu P; Giger ML
    Cancer Imaging; 2019 Sep; 19(1):64. PubMed ID: 31533838
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.