These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

302 related articles for article (PubMed ID: 35015305)

  • 1. Hybrid-attention densely connected U-Net with GAP for extracting livers from CT volumes.
    Chen Y; Hu F; Wang Y; Zheng C
    Med Phys; 2022 Feb; 49(2):1015-1033. PubMed ID: 35015305
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Hybrid dilation and attention residual U-Net for medical image segmentation.
    Wang Z; Zou Y; Liu PX
    Comput Biol Med; 2021 Jul; 134():104449. PubMed ID: 33993015
    [TBL] [Abstract][Full Text] [Related]  

  • 3. SAR-U-Net: Squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography.
    Wang J; Lv P; Wang H; Shi C
    Comput Methods Programs Biomed; 2021 Sep; 208():106268. PubMed ID: 34274611
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Modified U-Net (mU-Net) With Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images.
    Seo H; Huang C; Bassenne M; Xiao R; Xing L
    IEEE Trans Med Imaging; 2020 May; 39(5):1316-1325. PubMed ID: 31634827
    [TBL] [Abstract][Full Text] [Related]  

  • 5. GCHA-Net: Global context and hybrid attention network for automatic liver segmentation.
    Liu H; Fu Y; Zhang S; Liu J; Wang Y; Wang G; Fang J
    Comput Biol Med; 2023 Jan; 152():106352. PubMed ID: 36481761
    [TBL] [Abstract][Full Text] [Related]  

  • 6. MADR-Net: multi-level attention dilated residual neural network for segmentation of medical images.
    Balraj K; Ramteke M; Mittal S; Bhargava R; Rathore AS
    Sci Rep; 2024 Jun; 14(1):12699. PubMed ID: 38830932
    [TBL] [Abstract][Full Text] [Related]  

  • 7. HFRU-Net: High-Level Feature Fusion and Recalibration UNet for Automatic Liver and Tumor Segmentation in CT Images.
    Kushnure DT; Talbar SN
    Comput Methods Programs Biomed; 2022 Jan; 213():106501. PubMed ID: 34752959
    [TBL] [Abstract][Full Text] [Related]  

  • 8. RDCTrans U-Net: A Hybrid Variable Architecture for Liver CT Image Segmentation.
    Li L; Ma H
    Sensors (Basel); 2022 Mar; 22(7):. PubMed ID: 35408067
    [TBL] [Abstract][Full Text] [Related]  

  • 9. ASD-Net: a novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation.
    Ji Z; Mu J; Liu J; Zhang H; Dai C; Zhang X; Ganchev I
    Med Biol Eng Comput; 2024 Jun; 62(6):1673-1687. PubMed ID: 38326677
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A multiple-channel and atrous convolution network for ultrasound image segmentation.
    Zhang L; Zhang J; Li Z; Song Y
    Med Phys; 2020 Dec; 47(12):6270-6285. PubMed ID: 33007105
    [TBL] [Abstract][Full Text] [Related]  

  • 11. CAM-Wnet: An effective solution for accurate pulmonary embolism segmentation.
    Liu Z; Yuan H; Wang H
    Med Phys; 2022 Aug; 49(8):5294-5303. PubMed ID: 35609213
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Automatic liver tumor segmentation used the cascade multi-scale attention architecture method based on 3D U-Net.
    Wu Y; Shen H; Tan Y; Shi Y
    Int J Comput Assist Radiol Surg; 2022 Oct; 17(10):1915-1922. PubMed ID: 35672595
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Automatic Liver Segmentation Using EfficientNet and Attention-Based Residual U-Net in CT.
    Wang J; Zhang X; Lv P; Wang H; Cheng Y
    J Digit Imaging; 2022 Dec; 35(6):1479-1493. PubMed ID: 35711074
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Automatic mandible segmentation from CT image using 3D fully convolutional neural network based on DenseASPP and attention gates.
    Xu J; Liu J; Zhang D; Zhou Z; Jiang X; Zhang C; Chen X
    Int J Comput Assist Radiol Surg; 2021 Oct; 16(10):1785-1794. PubMed ID: 34287750
    [TBL] [Abstract][Full Text] [Related]  

  • 15. RA V-Net: deep learning network for automated liver segmentation.
    Lee Z; Qi S; Fan C; Xie Z; Meng J
    Phys Med Biol; 2022 Jun; 67(12):. PubMed ID: 35588720
    [No Abstract]   [Full Text] [Related]  

  • 16. A Multi-Scale Liver Tumor Segmentation Method Based on Residual and Hybrid Attention Enhanced Network with Contextual Integration.
    Sun L; Jiang L; Wang M; Wang Z; Xin Y
    Sensors (Basel); 2024 Sep; 24(17):. PubMed ID: 39275756
    [TBL] [Abstract][Full Text] [Related]  

  • 17. DENSE-INception U-net for medical image segmentation.
    Zhang Z; Wu C; Coleman S; Kerr D
    Comput Methods Programs Biomed; 2020 Aug; 192():105395. PubMed ID: 32163817
    [TBL] [Abstract][Full Text] [Related]  

  • 18. ResTransUnet: An effective network combined with Transformer and U-Net for liver segmentation in CT scans.
    Ou J; Jiang L; Bai T; Zhan P; Liu R; Xiao H
    Comput Biol Med; 2024 Jul; 177():108625. PubMed ID: 38823365
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Densely Connected U-Net With Criss-Cross Attention for Automatic Liver Tumor Segmentation in CT Images.
    Li Q; Song H; Wei Z; Yang F; Fan J; Ai D; Lin Y; Yu X; Yang J
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(6):3399-3410. PubMed ID: 35984790
    [TBL] [Abstract][Full Text] [Related]  

  • 20. TD-Net: A Hybrid End-to-End Network for Automatic Liver Tumor Segmentation From CT Images.
    Di S; Zhao YQ; Liao M; Zhang F; Li X
    IEEE J Biomed Health Inform; 2023 Mar; 27(3):1163-1172. PubMed ID: 35696476
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.