BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

138 related articles for article (PubMed ID: 36994940)

  • 1. Improved breast lesion detection in mammogram images using a deep neural network.
    Zhou W; Zhang X; Ding J; Deng L; Cheng G; Wang X
    Diagn Interv Radiol; 2023 Jul; 29(4):588-595. PubMed ID: 36994940
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Clinical application of convolutional neural network for mass analysis on mammograms.
    Li L; Lin X; Liao T; Ouyang R; Li M; Yuan J; Ma J
    Quant Imaging Med Surg; 2023 Dec; 13(12):8413-8422. PubMed ID: 38106316
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening.
    Liu H; Chen Y; Zhang Y; Wang L; Luo R; Wu H; Wu C; Zhang H; Tan W; Yin H; Wang D
    Eur Radiol; 2021 Aug; 31(8):5902-5912. PubMed ID: 33496829
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. A comparison of full-field digital mammograms versus 2D synthesized mammograms for detection of microcalcifications on screening.
    Wahab RA; Lee SJ; Zhang B; Sobel L; Mahoney MC
    Eur J Radiol; 2018 Oct; 107():14-19. PubMed ID: 30292258
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer.
    Becker AS; Marcon M; Ghafoor S; Wurnig MC; Frauenfelder T; Boss A
    Invest Radiol; 2017 Jul; 52(7):434-440. PubMed ID: 28212138
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. 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]  

  • 9. The added value of S-detect in the diagnostic accuracy of breast masses by senior and junior radiologist groups: a systematic review and meta-analysis.
    Chen P; Tong J; Lin T; Wang Y; Yu Y; Chen M; Yang G
    Gland Surg; 2022 Dec; 11(12):1946-1960. PubMed ID: 36654955
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Independent evaluation of computer classification of malignant and benign calcifications in full-field digital mammograms.
    Rana RS; Jiang Y; Schmidt RA; Nishikawa RM; Liu B
    Acad Radiol; 2007 Mar; 14(3):363-70. PubMed ID: 17307670
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A combined ultrasonic B-mode and color Doppler system for the classification of breast masses using neural network.
    Qian X; Zhang B; Liu S; Wang Y; Chen X; Liu J; Yang Y; Chen X; Wei Y; Xiao Q; Ma J; Shung KK; Zhou Q; Liu L; Chen Z
    Eur Radiol; 2020 May; 30(5):3023-3033. PubMed ID: 32006174
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists' Diagnosis Performance? An Observer Study.
    He Z; Li Y; Zeng W; Xu W; Liu J; Ma X; Wei J; Zeng H; Xu Z; Wang S; Wen C; Wu J; Feng C; Ma M; Qin G; Lu Y; Chen W
    Front Oncol; 2021; 11():773389. PubMed ID: 34976817
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Deep feature-based automatic classification of mammograms.
    Arora R; Rai PK; Raman B
    Med Biol Eng Comput; 2020 Jun; 58(6):1199-1211. PubMed ID: 32200453
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Deep learning performance for detection and classification of microcalcifications on mammography.
    Pesapane F; Trentin C; Ferrari F; Signorelli G; Tantrige P; Montesano M; Cicala C; Virgoli R; D'Acquisto S; Nicosia L; Origgi D; Cassano E
    Eur Radiol Exp; 2023 Nov; 7(1):69. PubMed ID: 37934382
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective.
    Mohamed AA; Luo Y; Peng H; Jankowitz RC; Wu S
    J Digit Imaging; 2018 Aug; 31(4):387-392. PubMed ID: 28932980
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Classification of asymmetry in mammography via the DenseNet convolutional neural network.
    Liao T; Li L; Ouyang R; Lin X; Lai X; Cheng G; Ma J
    Eur J Radiol Open; 2023 Dec; 11():100502. PubMed ID: 37448557
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.
    Hinton B; Ma L; Mahmoudzadeh AP; Malkov S; Fan B; Greenwood H; Joe B; Lee V; Kerlikowske K; Shepherd J
    Cancer Imaging; 2019 Jun; 19(1):41. PubMed ID: 31228956
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Automated pectoral muscle identification on MLO-view mammograms: Comparison of deep neural network to conventional computer vision.
    Ma X; Wei J; Zhou C; Helvie MA; Chan HP; Hadjiiski LM; Lu Y
    Med Phys; 2019 May; 46(5):2103-2114. PubMed ID: 30771257
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Detection and Weak Segmentation of Masses in Gray-Scale Breast Mammogram Images Using Deep Learning.
    Kim YJ; Kim KG
    Yonsei Med J; 2022 Jan; 63(Suppl):S63-S73. PubMed ID: 35040607
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.