BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

119 related articles for article (PubMed ID: 38642628)

  • 1. MRUNet-3D: A multi-stride residual 3D UNet for lung nodule segmentation.
    Bbosa R; Gui H; Luo F; Liu F; Efio-Akolly K; Chen YP
    Methods; 2024 Jun; 226():89-101. PubMed ID: 38642628
    [TBL] [Abstract][Full Text] [Related]  

  • 2. SK-Unet++: An improved Unet++ network with adaptive receptive fields for automatic segmentation of ultrasound thyroid nodule images.
    Dai H; Xie W; Xia E
    Med Phys; 2024 Mar; 51(3):1798-1811. PubMed ID: 37606374
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Two-stage lung nodule detection framework using enhanced UNet and convolutional LSTM networks in CT images.
    Akila Agnes S; Anitha J; Arun Solomon A
    Comput Biol Med; 2022 Oct; 149():106059. PubMed ID: 36087510
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Segmentation of pulmonary nodules in CT images based on 3D-UNET combined with three-dimensional conditional random field optimization.
    Wu W; Gao L; Duan H; Huang G; Ye X; Nie S
    Med Phys; 2020 Sep; 47(9):4054-4063. PubMed ID: 32428969
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Multi-view secondary input collaborative deep learning for lung nodule 3D segmentation.
    Dong X; Xu S; Liu Y; Wang A; Saripan MI; Li L; Zhang X; Lu L
    Cancer Imaging; 2020 Aug; 20(1):53. PubMed ID: 32738913
    [TBL] [Abstract][Full Text] [Related]  

  • 6. [Fully Automatic Glioma Segmentation Algorithm of Magnetic Resonance Imaging Based on 3D-UNet With More Global Contextual Feature Extraction: An Improvement on Insufficient Extraction of Global Features].
    Tian H; Wang Y; Ji Y; Rahman MM
    Sichuan Da Xue Xue Bao Yi Xue Ban; 2024 Mar; 55(2):447-454. PubMed ID: 38645864
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 3D multi-scale deep convolutional neural networks for pulmonary nodule detection.
    Peng H; Sun H; Guo Y
    PLoS One; 2021; 16(1):e0244406. PubMed ID: 33411741
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A Segmentation Framework of Pulmonary Nodules in Lung CT Images.
    Mukhopadhyay S
    J Digit Imaging; 2016 Feb; 29(1):86-103. PubMed ID: 26055544
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Deep Deconvolutional Residual Network Based Automatic Lung Nodule Segmentation.
    Singadkar G; Mahajan A; Thakur M; Talbar S
    J Digit Imaging; 2020 Jun; 33(3):678-684. PubMed ID: 32026218
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Development of a modified 3D region proposal network for lung nodule detection in computed tomography scans: a secondary analysis of lung nodule datasets.
    Lin CY; Guo SM; Lien JJ; Tsai TY; Liu YS; Lai CH; Hsu IL; Chang CC; Tseng YL
    Cancer Imaging; 2024 Mar; 24(1):40. PubMed ID: 38509635
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Efficient multiscale fully convolutional UNet model for segmentation of 3D lung nodule from CT image.
    Agnes SA; Anitha J
    J Med Imaging (Bellingham); 2022 Sep; 9(5):052402. PubMed ID: 35573467
    [No Abstract]   [Full Text] [Related]  

  • 12. Pulmonary nodule detection based on IR-UNet +  + .
    Lin J; She Q; Chen Y
    Med Biol Eng Comput; 2023 Feb; 61(2):485-495. PubMed ID: 36522521
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A Dual-Branch Framework With Prior Knowledge for Precise Segmentation of Lung Nodules in Challenging CT Scans.
    Jiang W; Zhi L; Zhang S; Zhou T
    IEEE J Biomed Health Inform; 2024 Mar; 28(3):1540-1551. PubMed ID: 38227405
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A Cascaded Multi-Stage Framework for Automatic Detection and Segmentation of Pulmonary Nodules in Developing Countries.
    Zhou Z; Gou F; Tan Y; Wu J
    IEEE J Biomed Health Inform; 2022 Nov; 26(11):5619-5630. PubMed ID: 35984795
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs.
    Gu Y; Lu X; Yang L; Zhang B; Yu D; Zhao Y; Gao L; Wu L; Zhou T
    Comput Biol Med; 2018 Dec; 103():220-231. PubMed ID: 30390571
    [TBL] [Abstract][Full Text] [Related]  

  • 16. MSDS-UNet: A multi-scale deeply supervised 3D U-Net for automatic segmentation of lung tumor in CT.
    Yang J; Wu B; Li L; Cao P; Zaiane O
    Comput Med Imaging Graph; 2021 Sep; 92():101957. PubMed ID: 34325225
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Multi-scale segmentation squeeze-and-excitation UNet with conditional random field for segmenting lung tumor from CT images.
    Zhang B; Qi S; Wu Y; Pan X; Yao Y; Qian W; Guan Y
    Comput Methods Programs Biomed; 2022 Jul; 222():106946. PubMed ID: 35716533
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Effect of segmentation algorithms on the performance of computerized detection of lung nodules in CT.
    Guo W; Li Q
    Med Phys; 2014 Sep; 41(9):091906. PubMed ID: 25186393
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Robust pulmonary nodule segmentation in CT: improving performance for juxtapleural cases.
    Okada K; Ramesh V; Krishnan A; Singh M; Akdemir U
    Med Image Comput Comput Assist Interv; 2005; 8(Pt 2):781-9. PubMed ID: 16686031
    [TBL] [Abstract][Full Text] [Related]  

  • 20. An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT.
    Alilou M; Beig N; Orooji M; Rajiah P; Velcheti V; Rakshit S; Reddy N; Yang M; Jacono F; Gilkeson RC; Linden P; Madabhushi A
    Med Phys; 2017 Jul; 44(7):3556-3569. PubMed ID: 28295386
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.