BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

521 related articles for article (PubMed ID: 30473474)

  • 1. Computer-aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features.
    Zheng Q; Furth SL; Tasian GE; Fan Y
    J Pediatr Urol; 2019 Feb; 15(1):75.e1-75.e7. PubMed ID: 30473474
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Multi-instance Deep Learning of Ultrasound Imaging Data for Pattern Classification of Congenital Abnormalities of the Kidney and Urinary Tract in Children.
    Yin S; Peng Q; Li H; Zhang Z; You X; Fischer K; Furth SL; Fan Y; Tasian GE
    Urology; 2020 Aug; 142():183-189. PubMed ID: 32445770
    [TBL] [Abstract][Full Text] [Related]  

  • 3. TRANSFER LEARNING FOR DIAGNOSIS OF CONGENITAL ABNORMALITIES OF THE KIDNEY AND URINARY TRACT IN CHILDREN BASED ON ULTRASOUND IMAGING DATA.
    Zheng Q; Tasian G; Fan Y
    Proc IEEE Int Symp Biomed Imaging; 2018 Apr; 2018():1487-1490. PubMed ID: 30079128
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Computer-Aided Diagnosis of Congenital Abnormalities of the Kidney and Urinary Tract in Children Using a Multi-Instance Deep Learning Method Based on Ultrasound Imaging Data.
    Yin S; Peng Q; Li H; Zhang Z; You X; Fischer K; Furth SL; Tasian GE; Fan Y
    Proc IEEE Int Symp Biomed Imaging; 2020 Apr; 2020():1347-1350. PubMed ID: 33850604
    [TBL] [Abstract][Full Text] [Related]  

  • 5. [Incidence, diagnosis and treatment of children's congenital abnormalities of the kidney and urinary tract detected in ultrasound screening].
    Zhang B; Wang H; Sun N; Jia LQ; Shen Y
    Zhonghua Er Ke Za Zhi; 2011 Jul; 49(7):534-8. PubMed ID: 22088185
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.
    Lee H; Hong H; Kim J; Jung DC
    Med Phys; 2018 Apr; 45(4):1550-1561. PubMed ID: 29474742
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification.
    Lee HS; Hong H; Jung DC; Park S; Kim J
    Med Phys; 2017 Jul; 44(7):3604-3614. PubMed ID: 28376281
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Automatic Screening of Pediatric Renal Ultrasound Abnormalities: Deep Learning and Transfer Learning Approach.
    Tsai MC; Lu HH; Chang YC; Huang YC; Fu LS
    JMIR Med Inform; 2022 Nov; 10(11):e40878. PubMed ID: 36322109
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Congenital Anomalies of the Kidney and Urinary Tract (CAKUT).
    Ristoska-Bojkovska N
    Pril (Makedon Akad Nauk Umet Odd Med Nauki); 2017 Mar; 38(1):59-62. PubMed ID: 28593883
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Deep learning for patient-specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks.
    Nyflot MJ; Thammasorn P; Wootton LS; Ford EC; Chaovalitwongse WA
    Med Phys; 2019 Feb; 46(2):456-464. PubMed ID: 30548601
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Patch-based classification of thyroid nodules in ultrasound images using direction independent features extracted by two-threshold binary decomposition.
    Prochazka A; Gulati S; Holinka S; Smutek D
    Comput Med Imaging Graph; 2019 Jan; 71():9-18. PubMed ID: 30453231
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Machine Learning-Aided Chronic Kidney Disease Diagnosis Based on Ultrasound Imaging Integrated with Computer-Extracted Measurable Features.
    Lee S; Kang M; Byeon K; Lee SE; Lee IH; Kim YA; Kang SW; Park JT
    J Digit Imaging; 2022 Oct; 35(5):1091-1100. PubMed ID: 35411524
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier.
    Treder M; Lauermann JL; Eter N
    Graefes Arch Clin Exp Ophthalmol; 2018 Nov; 256(11):2053-2060. PubMed ID: 30091055
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Congenital Anomalies of the Kidney and Urinary Tract in Children Born Small for Gestational Age.
    Janchevska A; Gucev Z; Tasevska-Rmus L; Tasic V
    Pril (Makedon Akad Nauk Umet Odd Med Nauki); 2017 Mar; 38(1):53-57. PubMed ID: 28593895
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Congenital Anomalies of the Kidneys and Urinary Tract.
    Stein D; McNamara E
    Clin Perinatol; 2022 Sep; 49(3):791-798. PubMed ID: 36113935
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI.
    Hu Q; Whitney HM; Giger ML
    Sci Rep; 2020 Jun; 10(1):10536. PubMed ID: 32601367
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Cascade marker removal algorithm for thyroid ultrasound images.
    Ying X; Zhang Y; Yu M; Wei X; Zhu J; Gao J; Liu Z; Shen H; Zhang R; Li X; Yu R
    Med Biol Eng Comput; 2020 Nov; 58(11):2641-2656. PubMed ID: 32840765
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A transfer learning method with deep residual network for pediatric pneumonia diagnosis.
    Liang G; Zheng L
    Comput Methods Programs Biomed; 2020 Apr; 187():104964. PubMed ID: 31262537
    [TBL] [Abstract][Full Text] [Related]  

  • 19. What Did We Find From Imaging Studies in Childhood Urinary Tract Infection and Which Studies Are Mandatory?
    Vachvanichsanong P; Dissaneewate P; McNeil E
    Urology; 2018 Jan; 111():176-182. PubMed ID: 28982546
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Ultrasound mass screening for congenital anomalies of the kidney and urinary tract.
    Caiulo VA; Caiulo S; Gargasole C; Chiriacò G; Latini G; Cataldi L; Mele G
    Pediatr Nephrol; 2012 Jun; 27(6):949-53. PubMed ID: 22271367
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 27.