BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

109 related articles for article (PubMed ID: 38134751)

  • 1. SaB-Net: Self-attention backward network for gastric tumor segmentation in CT images.
    He J; Zhang M; Li W; Peng Y; Fu B; Liu C; Wang J; Wang R
    Comput Biol Med; 2024 Feb; 169():107866. PubMed ID: 38134751
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 3D Multi-Attention Guided Multi-Task Learning Network for Automatic Gastric Tumor Segmentation and Lymph Node Classification.
    Zhang Y; Li H; Du J; Qin J; Wang T; Chen Y; Liu B; Gao W; Ma G; Lei B
    IEEE Trans Med Imaging; 2021 Jun; 40(6):1618-1631. PubMed ID: 33646948
    [TBL] [Abstract][Full Text] [Related]  

  • 3. ARPM-net: A novel CNN-based adversarial method with Markov random field enhancement for prostate and organs at risk segmentation in pelvic CT images.
    Zhang Z; Zhao T; Gay H; Zhang W; Sun B
    Med Phys; 2021 Jan; 48(1):227-237. PubMed ID: 33151620
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. PA-Net: A phase attention network fusing venous and arterial phase features of CT images for liver tumor segmentation.
    Liu Z; Hou J; Pan X; Zhang R; Shi Z
    Comput Methods Programs Biomed; 2024 Feb; 244():107997. PubMed ID: 38176329
    [TBL] [Abstract][Full Text] [Related]  

  • 6. PA-ResSeg: A phase attention residual network for liver tumor segmentation from multiphase CT images.
    Xu Y; Cai M; Lin L; Zhang Y; Hu H; Peng Z; Zhang Q; Chen Q; Mao X; Iwamoto Y; Han XH; Chen YW; Tong R
    Med Phys; 2021 Jul; 48(7):3752-3766. PubMed ID: 33950526
    [TBL] [Abstract][Full Text] [Related]  

  • 7. RMAU-Net: Residual Multi-Scale Attention U-Net For liver and tumor segmentation in CT images.
    Jiang L; Ou J; Liu R; Zou Y; Xie T; Xiao H; Bai T
    Comput Biol Med; 2023 May; 158():106838. PubMed ID: 37030263
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A novel adaptive cubic quasi-Newton optimizer for deep learning based medical image analysis tasks, validated on detection of COVID-19 and segmentation for COVID-19 lung infection, liver tumor, and optic disc/cup.
    Liu Y; Zhang M; Zhong Z; Zeng X
    Med Phys; 2023 Mar; 50(3):1528-1538. PubMed ID: 36057788
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Attention Connect Network for Liver Tumor Segmentation from CT and MRI Images.
    Shao J; Luan S; Ding Y; Xue X; Zhu B; Wei W
    Technol Cancer Res Treat; 2024; 23():15330338231219366. PubMed ID: 38179668
    [No Abstract]   [Full Text] [Related]  

  • 10. Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets.
    Jiang J; Hu YC; Tyagi N; Zhang P; Rimner A; Deasy JO; Veeraraghavan H
    Med Phys; 2019 Oct; 46(10):4392-4404. PubMed ID: 31274206
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Weaving attention U-net: A novel hybrid CNN and attention-based method for organs-at-risk segmentation in head and neck CT images.
    Zhang Z; Zhao T; Gay H; Zhang W; Sun B
    Med Phys; 2021 Nov; 48(11):7052-7062. PubMed ID: 34655077
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Automatic segmentation of organs at risk and tumors in CT images of lung cancer from partially labelled datasets with a semi-supervised conditional nnU-Net.
    Zhang G; Yang Z; Huo B; Chai S; Jiang S
    Comput Methods Programs Biomed; 2021 Nov; 211():106419. PubMed ID: 34563895
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Self-channel-and-spatial-attention neural network for automated multi-organ segmentation on head and neck CT images.
    Gou S; Tong N; Qi S; Yang S; Chin R; Sheng K
    Phys Med Biol; 2020 Dec; 65(24):245034. PubMed ID: 32097892
    [TBL] [Abstract][Full Text] [Related]  

  • 15. MANet: a multi-attention network for automatic liver tumor segmentation in computed tomography (CT) imaging.
    Hettihewa K; Kobchaisawat T; Tanpowpong N; Chalidabhongse TH
    Sci Rep; 2023 Nov; 13(1):20098. PubMed ID: 37973987
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Lung tumor segmentation in 4D CT images using motion convolutional neural networks.
    Momin S; Lei Y; Tian Z; Wang T; Roper J; Kesarwala AH; Higgins K; Bradley JD; Liu T; Yang X
    Med Phys; 2021 Nov; 48(11):7141-7153. PubMed ID: 34469001
    [TBL] [Abstract][Full Text] [Related]  

  • 17. An effective deep network for automatic segmentation of complex lung tumors in CT images.
    Wang B; Chen K; Tian X; Yang Y; Zhang X
    Med Phys; 2021 Sep; 48(9):5004-5016. PubMed ID: 34224147
    [TBL] [Abstract][Full Text] [Related]  

  • 18. BiFTransNet: A unified and simultaneous segmentation network for gastrointestinal images of CT & MRI.
    Jiang X; Ding Y; Liu M; Wang Y; Li Y; Wu Z
    Comput Biol Med; 2023 Oct; 165():107326. PubMed ID: 37619324
    [TBL] [Abstract][Full Text] [Related]  

  • 19. RSU-Net: U-net based on residual and self-attention mechanism in the segmentation of cardiac magnetic resonance images.
    Li YZ; Wang Y; Huang YH; Xiang P; Liu WX; Lai QQ; Gao YY; Xu MS; Guo YF
    Comput Methods Programs Biomed; 2023 Apr; 231():107437. PubMed ID: 36863157
    [TBL] [Abstract][Full Text] [Related]  

  • 20. AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy.
    Zhu W; Huang Y; Zeng L; Chen X; Liu Y; Qian Z; Du N; Fan W; Xie X
    Med Phys; 2019 Feb; 46(2):576-589. PubMed ID: 30480818
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.