BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

1161 related articles for article (PubMed ID: 33950526)

  • 41. 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]  

  • 42. A deep residual attention-based U-Net with a biplane joint method for liver segmentation from CT scans.
    Chen Y; Zheng C; Zhou T; Feng L; Liu L; Zeng Q; Wang G
    Comput Biol Med; 2023 Jan; 152():106421. PubMed ID: 36527780
    [TBL] [Abstract][Full Text] [Related]  

  • 43. LSAM: L2-norm self-attention and latent space feature interaction for automatic 3D multi-modal head and neck tumor segmentation.
    Li L; Tan J; Yu L; Li C; Nan H; Zheng S
    Phys Med Biol; 2023 Nov; 68(22):. PubMed ID: 37852283
    [No Abstract]   [Full Text] [Related]  

  • 44. Decoupled pyramid correlation network for liver tumor segmentation from CT images.
    Zhang Y; Yang J; Liu Y; Tian J; Wang S; Zhong C; Shi Z; Zhang Y; He Z
    Med Phys; 2022 Nov; 49(11):7207-7221. PubMed ID: 35620834
    [TBL] [Abstract][Full Text] [Related]  

  • 45. Multi-scale attention and deep supervision-based 3D UNet for automatic liver segmentation from CT.
    Wang J; Zhang X; Guo L; Shi C; Tamura S
    Math Biosci Eng; 2023 Jan; 20(1):1297-1316. PubMed ID: 36650812
    [TBL] [Abstract][Full Text] [Related]  

  • 46. MRLA-Net: A tumor segmentation network embedded with a multiple receptive-field lesion attention module in PET-CT images.
    Zhou Y; Jiang H; Diao Z; Tong G; Luan Q; Li Y; Li X
    Comput Biol Med; 2023 Feb; 153():106538. PubMed ID: 36646023
    [TBL] [Abstract][Full Text] [Related]  

  • 47. MS-FANet: Multi-scale feature attention network for liver tumor segmentation.
    Chen Y; Zheng C; Zhang W; Lin H; Chen W; Zhang G; Xu G; Wu F
    Comput Biol Med; 2023 Sep; 163():107208. PubMed ID: 37421737
    [TBL] [Abstract][Full Text] [Related]  

  • 48. 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]  

  • 49. Liver segmentation in abdominal CT images via auto-context neural network and self-supervised contour attention.
    Chung M; Lee J; Park S; Lee CE; Lee J; Shin YG
    Artif Intell Med; 2021 Mar; 113():102023. PubMed ID: 33685586
    [TBL] [Abstract][Full Text] [Related]  

  • 50. Content-based image retrieval of multiphase CT images for focal liver lesion characterization.
    Chi Y; Zhou J; Venkatesh SK; Tian Q; Liu J
    Med Phys; 2013 Oct; 40(10):103502. PubMed ID: 24089935
    [TBL] [Abstract][Full Text] [Related]  

  • 51. HCA-DAN: hierarchical class-aware domain adaptive network for gastric tumor segmentation in 3D CT images.
    Yuan N; Zhang Y; Lv K; Liu Y; Yang A; Hu P; Yu H; Han X; Guo X; Li J; Wang T; Lei B; Ma G
    Cancer Imaging; 2024 May; 24(1):63. PubMed ID: 38773670
    [TBL] [Abstract][Full Text] [Related]  

  • 52. Applicability of multidimensional convolutional neural networks on automated detection of diverse focal liver lesions in multiphase CT images.
    Chen Q; Zhu Y; Chen Y; Wang F; Hu X; Ye Y; Dou X; Huang Y; Deng L; Zhou W; Liang X; Hu H
    Med Phys; 2023 May; 50(5):2872-2883. PubMed ID: 36441108
    [TBL] [Abstract][Full Text] [Related]  

  • 53. Liver tumor segmentation based on 3D convolutional neural network with dual scale.
    Meng L; Tian Y; Bu S
    J Appl Clin Med Phys; 2020 Jan; 21(1):144-157. PubMed ID: 31793212
    [TBL] [Abstract][Full Text] [Related]  

  • 54. A robust and automatic CT-3D ultrasound registration method based on segmentation, context, and edge hybrid metric.
    He B; Zhao S; Dai Y; Wu J; Luo H; Guo J; Ni Z; Wu T; Kuang F; Jiang H; Zhang Y; Jia F
    Med Phys; 2023 Oct; 50(10):6243-6258. PubMed ID: 36975007
    [TBL] [Abstract][Full Text] [Related]  

  • 55. An improved 3D KiU-Net for segmentation of liver tumor.
    Chen G; Li Z; Wang J; Wang J; Du S; Zhou J; Shi J; Zhou Y
    Comput Biol Med; 2023 Jun; 160():107006. PubMed ID: 37159962
    [TBL] [Abstract][Full Text] [Related]  

  • 56. Dual-path Network for Liver and Tumor Segmentation in CT Images Using Swin Transformer Encoding Approach.
    Yang Z; Li S
    Curr Med Imaging; 2023; 19(10):1114-1123. PubMed ID: 36239728
    [TBL] [Abstract][Full Text] [Related]  

  • 57. Context fusion network with multi-scale-aware skip connection and twin-split attention for liver tumor segmentation.
    Wang Z; Zhu J; Fu S; Ye Y
    Med Biol Eng Comput; 2023 Dec; 61(12):3167-3180. PubMed ID: 37470963
    [TBL] [Abstract][Full Text] [Related]  

  • 58. Tumor attention networks: Better feature selection, better tumor segmentation.
    Pang S; Du A; Orgun MA; Wang Y; Yu Z
    Neural Netw; 2021 Aug; 140():203-222. PubMed ID: 33780873
    [TBL] [Abstract][Full Text] [Related]  

  • 59. Eres-UNet++: Liver CT image segmentation based on high-efficiency channel attention and Res-UNet+.
    Li J; Liu K; Hu Y; Zhang H; Heidari AA; Chen H; Zhang W; Algarni AD; Elmannai H
    Comput Biol Med; 2023 May; 158():106501. PubMed ID: 36635120
    [TBL] [Abstract][Full Text] [Related]  

  • 60. 3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy
    Wu W; Wu S; Zhou Z; Zhang R; Zhang Y
    Biomed Res Int; 2017; 2017():5207685. PubMed ID: 29090220
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 59.