BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

1211 related articles for article (PubMed ID: 33950526)

  • 21. Domain-Adversarial Transformer Network for Multiphase Liver Tumor Segmentation.
    Ni Y; Chen G; Feng Z; Cui H; Metaxas D; Zhang S; Zhu W
    Annu Int Conf IEEE Eng Med Biol Soc; 2023 Jul; 2023():1-4. PubMed ID: 38083011
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Liver tumor segmentation in CT volumes using an adversarial densely connected network.
    Chen L; Song H; Wang C; Cui Y; Yang J; Hu X; Zhang L
    BMC Bioinformatics; 2019 Dec; 20(Suppl 16):587. PubMed ID: 31787071
    [TBL] [Abstract][Full Text] [Related]  

  • 23. HMA-Net: A deep U-shaped network combined with HarDNet and multi-attention mechanism for medical image segmentation.
    Liu Q; Han Z; Liu Z; Zhang J
    Med Phys; 2023 Mar; 50(3):1635-1646. PubMed ID: 36303466
    [TBL] [Abstract][Full Text] [Related]  

  • 24. ABCNet: A new efficient 3D dense-structure network for segmentation and analysis of body tissue composition on body-torso-wide CT images.
    Liu T; Pan J; Torigian DA; Xu P; Miao Q; Tong Y; Udupa JK
    Med Phys; 2020 Jul; 47(7):2986-2999. PubMed ID: 32170754
    [TBL] [Abstract][Full Text] [Related]  

  • 25. MSRA-Net: Tumor segmentation network based on Multi-scale Residual Attention.
    Wu Y; Jiang H; Pang W
    Comput Biol Med; 2023 May; 158():106818. PubMed ID: 36966557
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Vessel segmentation from volumetric images: a multi-scale double-pathway network with class-balanced loss at the voxel level.
    Chen Y; Fan S; Chen Y; Che C; Cao X; He X; Song X; Zhao F
    Med Phys; 2021 Jul; 48(7):3804-3814. PubMed ID: 33969487
    [TBL] [Abstract][Full Text] [Related]  

  • 27. A three-path network with multi-scale selective feature fusion, edge-inspiring and edge-guiding for liver tumor segmentation.
    Shui Y; Wang Z; Liu B; Wang W; Fu S; Li Y
    Comput Biol Med; 2024 Jan; 168():107841. PubMed ID: 38081117
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Tumor conspicuity enhancement-based segmentation model for liver tumor segmentation and RECIST diameter measurement in non-contrast CT images.
    Liu H; Zhou Y; Gou S; Luo Z
    Comput Biol Med; 2024 May; 174():108420. PubMed ID: 38613896
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Medical lesion segmentation by combining multimodal images with modality weighted UNet.
    Zhu X; Wu Y; Hu H; Zhuang X; Yao J; Ou D; Li W; Song M; Feng N; Xu D
    Med Phys; 2022 Jun; 49(6):3692-3704. PubMed ID: 35312077
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Recurrent feature fusion learning for multi-modality pet-ct tumor segmentation.
    Bi L; Fulham M; Li N; Liu Q; Song S; Dagan Feng D; Kim J
    Comput Methods Programs Biomed; 2021 May; 203():106043. PubMed ID: 33744750
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Adaptive Attention Convolutional Neural Network for Liver Tumor Segmentation.
    Luan S; Xue X; Ding Y; Wei W; Zhu B
    Front Oncol; 2021; 11():680807. PubMed ID: 34434891
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Residual based attention-Unet combing DAC and RMP modules for automatic liver tumor segmentation in CT.
    Bi R; Ji C; Yang Z; Qiao M; Lv P; Wang H
    Math Biosci Eng; 2022 Mar; 19(5):4703-4718. PubMed ID: 35430836
    [No Abstract]   [Full Text] [Related]  

  • 33. 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]  

  • 34. Pulmonary arteries segmentation from CT images using PA-Net with attention module and contour loss.
    Yuan C; Song S; Yang J; Sun Y; Yang B; Xu L
    Med Phys; 2023 Aug; 50(8):4887-4898. PubMed ID: 36752170
    [TBL] [Abstract][Full Text] [Related]  

  • 35. SADSNet: A robust 3D synchronous segmentation network for liver and liver tumors based on spatial attention mechanism and deep supervision.
    Yang S; Liang Y; Wu S; Sun P; Chen Z
    J Xray Sci Technol; 2024; 32(3):707-723. PubMed ID: 38552134
    [TBL] [Abstract][Full Text] [Related]  

  • 36. MFCNet: A multi-modal fusion and calibration networks for 3D pancreas tumor segmentation on PET-CT images.
    Wang F; Cheng C; Cao W; Wu Z; Wang H; Wei W; Yan Z; Liu Z
    Comput Biol Med; 2023 Mar; 155():106657. PubMed ID: 36791551
    [TBL] [Abstract][Full Text] [Related]  

  • 37. 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]  

  • 38. Shift-channel attention and weighted-region loss function for liver and dense tumor segmentation.
    Li J; Huang G; He J; Chen Z; Pun CM; Yu Z; Ling WK; Liu L; Zhou J; Huang J
    Med Phys; 2022 Nov; 49(11):7193-7206. PubMed ID: 35746843
    [TBL] [Abstract][Full Text] [Related]  

  • 39. 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]  

  • 40. MCAFNet: multiscale cross-layer attention fusion network for honeycomb lung lesion segmentation.
    Li G; Xie J; Zhang L; Sun M; Li Z; Sun Y
    Med Biol Eng Comput; 2024 Apr; 62(4):1121-1137. PubMed ID: 38150110
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 61.