BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

350 related articles for article (PubMed ID: 30137658)

  • 1. CNN as model observer in a liver lesion detection task for x-ray computed tomography: A phantom study.
    Kopp FK; Catalano M; Pfeiffer D; Fingerle AA; Rummeny EJ; Noël PB
    Med Phys; 2018 Oct; 45(10):4439-4447. PubMed ID: 30137658
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A deep learning- and partial least square regression-based model observer for a low-contrast lesion detection task in CT.
    Gong H; Yu L; Leng S; Dilger SK; Ren L; Zhou W; Fletcher JG; McCollough CH
    Med Phys; 2019 May; 46(5):2052-2063. PubMed ID: 30889282
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Correlation between a 2D channelized Hotelling observer and human observers in a low-contrast detection task with multislice reading in CT.
    Yu L; Chen B; Kofler JM; Favazza CP; Leng S; Kupinski MA; McCollough CH
    Med Phys; 2017 Aug; 44(8):3990-3999. PubMed ID: 28555878
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Localization of liver lesions in abdominal CT imaging: II. Mathematical model observer performance correlates with human observer performance for localization of liver lesions in abdominal CT imaging.
    Dilger SKN; Leng S; Chen B; Carter RE; Favazza CP; Fletcher JG; McCollough CH; Yu L
    Phys Med Biol; 2019 May; 64(10):105012. PubMed ID: 30995626
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: impact of radiation dose and reconstruction algorithms.
    Yu L; Leng S; Chen L; Kofler JM; Carter RE; McCollough CH
    Med Phys; 2013 Apr; 40(4):041908. PubMed ID: 23556902
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Correlation between model observer and human observer performance in CT imaging when lesion location is uncertain.
    Leng S; Yu L; Zhang Y; Carter R; Toledano AY; McCollough CH
    Med Phys; 2013 Aug; 40(8):081908. PubMed ID: 23927322
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Pre-whitened matched filter and convolutional neural network based model observer performance for mass lesion detection in non-contrast breast CT.
    Lyu SH; Abbey CK; Hernandez AM; Boone JM
    Med Phys; 2023 Dec; 50(12):7558-7567. PubMed ID: 37646463
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Deep-learning model observer for a low-contrast hepatic metastases localization task in computed tomography.
    Gong H; Fletcher JG; Heiken JP; Wells ML; Leng S; McCollough CH; Yu L
    Med Phys; 2022 Jan; 49(1):70-83. PubMed ID: 34792800
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A convolutional neural network-based model observer for breast CT images.
    Kim G; Han M; Shim H; Baek J
    Med Phys; 2020 Apr; 47(4):1619-1632. PubMed ID: 32017147
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Channelized hotelling and human observer correlation for lesion detection in hepatic SPECT imaging.
    Gifford HC; King MA; de Vries DJ; Soares EJ
    J Nucl Med; 2000 Mar; 41(3):514-21. PubMed ID: 10716327
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Effects of various generations of iterative CT reconstruction algorithms on low-contrast detectability as a function of the effective abdominal diameter: A quantitative task-based phantom study.
    Viry A; Aberle C; Racine D; Knebel JF; Schindera ST; Schmidt S; Becce F; Verdun FR
    Phys Med; 2018 Apr; 48():111-118. PubMed ID: 29728223
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Strategy to implement a convolutional neural network based ideal model observer via transfer learning for multi-slice simulated breast CT images.
    Kim G; Han M; Baek J
    Phys Med Biol; 2023 May; 68(11):. PubMed ID: 37137323
    [No Abstract]   [Full Text] [Related]  

  • 13. A convolutional neural network-based anthropomorphic model observer for signal-known-statistically and background-known-statistically detection tasks.
    Han M; Baek J
    Phys Med Biol; 2020 Nov; 65(22):225025. PubMed ID: 33032268
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Assessing image quality and dose reduction of a new x-ray computed tomography iterative reconstruction algorithm using model observers.
    Tseng HW; Fan J; Kupinski MA; Sainath P; Hsieh J
    Med Phys; 2014 Jul; 41(7):071910. PubMed ID: 24989388
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Evaluation of the impact of tube current modulation on lesion detectability using model observers.
    Wunderlich A; Noo F
    Annu Int Conf IEEE Eng Med Biol Soc; 2008; 2008():2705-8. PubMed ID: 19163263
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Localization of liver lesions in abdominal CT imaging: I. Correlation of human observer performance between anatomical and uniform backgrounds.
    Dilger SKN; Yu L; Chen B; Favazza CP; Carter RE; Fletcher JG; McCollough CH; Leng S
    Phys Med Biol; 2019 May; 64(10):105011. PubMed ID: 30995611
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Correlation between human observer performance and model observer performance in differential phase contrast CT.
    Li K; Garrett J; Chen GH
    Med Phys; 2013 Nov; 40(11):111905. PubMed ID: 24320438
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Correlation between human and model observer performance for discrimination task in CT.
    Zhang Y; Leng S; Yu L; Carter RE; McCollough CH
    Phys Med Biol; 2014 Jul; 59(13):3389-404. PubMed ID: 24875060
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study.
    Yasaka K; Akai H; Abe O; Kiryu S
    Radiology; 2018 Mar; 286(3):887-896. PubMed ID: 29059036
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A performance comparison of anthropomorphic model observers for breast cone beam CT images: A single-slice and multislice study.
    Han M; Baek J
    Med Phys; 2019 Aug; 46(8):3431-3441. PubMed ID: 31106432
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 18.