BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

294 related articles for article (PubMed ID: 35260710)

  • 1. Automated pancreas segmentation and volumetry using deep neural network on computed tomography.
    Lim SH; Kim YJ; Park YH; Kim D; Kim KG; Lee DH
    Sci Rep; 2022 Mar; 12(1):4075. PubMed ID: 35260710
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation.
    Roth HR; Lu L; Lay N; Harrison AP; Farag A; Sohn A; Summers RM
    Med Image Anal; 2018 Apr; 45():94-107. PubMed ID: 29427897
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Two-stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra-reader and inter-reader reliability at full and reduced radiation dose on an external dataset.
    Panda A; Korfiatis P; Suman G; Garg SK; Polley EC; Singh DP; Chari ST; Goenka AH
    Med Phys; 2021 May; 48(5):2468-2481. PubMed ID: 33595105
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Technical and Clinical Factors Affecting Success Rate of a Deep Learning Method for Pancreas Segmentation on CT.
    Bagheri MH; Roth H; Kovacs W; Yao J; Farhadi F; Li X; Summers RM
    Acad Radiol; 2020 May; 27(5):689-695. PubMed ID: 31537506
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Extension-contraction transformation network for pancreas segmentation in abdominal CT scans.
    Zheng Y; Luo J
    Comput Biol Med; 2023 Jan; 152():106410. PubMed ID: 36516578
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Pancreas Segmentation in Abdominal CT Scans using Inter-/Intra-Slice Contextual Information with a Cascade Neural Network.
    Yang Z; Zhang L; Zhang M; Feng J; Wu Z; Ren F; Lv Y
    Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():5937-5940. PubMed ID: 31947200
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Improving the slice interaction of 2.5D CNN for automatic pancreas segmentation.
    Zheng H; Qian L; Qin Y; Gu Y; Yang J
    Med Phys; 2020 Nov; 47(11):5543-5554. PubMed ID: 32502278
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A Two-Phase Approach using Mask R-CNN and 3D U-Net for High-Accuracy Automatic Segmentation of Pancreas in CT Imaging.
    Dogan RO; Dogan H; Bayrak C; Kayikcioglu T
    Comput Methods Programs Biomed; 2021 Aug; 207():106141. PubMed ID: 34020373
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Development and Validation of a Deep Learning System for Segmentation of Abdominal Muscle and Fat on Computed Tomography.
    Park HJ; Shin Y; Park J; Kim H; Lee IS; Seo DW; Huh J; Lee TY; Park T; Lee J; Kim KW
    Korean J Radiol; 2020 Jan; 21(1):88-100. PubMed ID: 31920032
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Automatic multi-label temporal bone computed tomography segmentation with deep learning.
    Zhou L; Li Z
    Int J Med Robot; 2023 Oct; 19(5):e2536. PubMed ID: 37203865
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Multiscale unsupervised domain adaptation for automatic pancreas segmentation in CT volumes using adversarial learning.
    Zhu Y; Hu P; Li X; Tian Y; Bai X; Liang T; Li J
    Med Phys; 2022 Sep; 49(9):5799-5818. PubMed ID: 35833617
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images.
    Fu M; Wu W; Hong X; Liu Q; Jiang J; Ou Y; Zhao Y; Gong X
    BMC Syst Biol; 2018 Apr; 12(Suppl 4):56. PubMed ID: 29745840
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Automated pancreatic segmentation and fat fraction evaluation based on a self-supervised transfer learning network.
    Zhang G; Zhan Q; Gao Q; Mao K; Yang P; Gao Y; Wang L; Song B; Chen Y; Bian Y; Shao C; Lu J; Ma C
    Comput Biol Med; 2024 Mar; 170():107989. PubMed ID: 38286105
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Graph-enhanced U-Net for semi-supervised segmentation of pancreas from abdomen CT scan.
    Liu S; Liang S; Huang X; Yuan X; Zhong T; Zhang Y
    Phys Med Biol; 2022 Jul; 67(15):. PubMed ID: 35892477
    [No Abstract]   [Full Text] [Related]  

  • 15. Automatic quantitative evaluation of normal pancreas based on deep learning in a Chinese adult population.
    Cai J; Guo X; Wang K; Zhang Y; Zhang D; Zhang X; Wang X
    Abdom Radiol (NY); 2022 Mar; 47(3):1082-1090. PubMed ID: 35064795
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Interactive 3D U-net for the segmentation of the pancreas in computed tomography scans.
    Boers TGW; Hu Y; Gibson E; Barratt DC; Bonmati E; Krdzalic J; van der Heijden F; Hermans JJ; Huisman HJ
    Phys Med Biol; 2020 Mar; 65(6):065002. PubMed ID: 31978921
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Dual center validation of deep learning for automated multi-label segmentation of thoracic anatomy in bedside chest radiographs.
    Busch F; Xu L; Sushko D; Weidlich M; Truhn D; Müller-Franzes G; Heimer MM; Niehues SM; Makowski MR; Hinsche M; Vahldiek JL; Aerts HJ; Adams LC; Bressem KK
    Comput Methods Programs Biomed; 2023 Jun; 234():107505. PubMed ID: 37003043
    [TBL] [Abstract][Full Text] [Related]  

  • 18. An application of cascaded 3D fully convolutional networks for medical image segmentation.
    Roth HR; Oda H; Zhou X; Shimizu N; Yang Y; Hayashi Y; Oda M; Fujiwara M; Misawa K; Mori K
    Comput Med Imaging Graph; 2018 Jun; 66():90-99. PubMed ID: 29573583
    [TBL] [Abstract][Full Text] [Related]  

  • 19. MAD-UNet: A deep U-shaped network combined with an attention mechanism for pancreas segmentation in CT images.
    Li W; Qin S; Li F; Wang L
    Med Phys; 2021 Jan; 48(1):329-341. PubMed ID: 33222222
    [TBL] [Abstract][Full Text] [Related]  

  • 20. CheXLocNet: Automatic localization of pneumothorax in chest radiographs using deep convolutional neural networks.
    Wang H; Gu H; Qin P; Wang J
    PLoS One; 2020; 15(11):e0242013. PubMed ID: 33166371
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.