BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

121 related articles for article (PubMed ID: 38517809)

  • 41. Artificial intelligence-based technology for semi-automated segmentation of rectal cancer using high-resolution MRI.
    Hamabe A; Ishii M; Kamoda R; Sasuga S; Okuya K; Okita K; Akizuki E; Sato Y; Miura R; Onodera K; Hatakenaka M; Takemasa I
    PLoS One; 2022; 17(6):e0269931. PubMed ID: 35714069
    [TBL] [Abstract][Full Text] [Related]  

  • 42. Cross-dimensional transfer learning in medical image segmentation with deep learning.
    Messaoudi H; Belaid A; Ben Salem D; Conze PH
    Med Image Anal; 2023 Aug; 88():102868. PubMed ID: 37384952
    [TBL] [Abstract][Full Text] [Related]  

  • 43. A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images.
    Bevilacqua V; Brunetti A; Cascarano GD; Guerriero A; Pesce F; Moschetta M; Gesualdo L
    BMC Med Inform Decis Mak; 2019 Dec; 19(Suppl 9):244. PubMed ID: 31830973
    [TBL] [Abstract][Full Text] [Related]  

  • 44. Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data.
    Gsaxner C; Roth PM; Wallner J; Egger J
    PLoS One; 2019; 14(3):e0212550. PubMed ID: 30835746
    [TBL] [Abstract][Full Text] [Related]  

  • 45. Deep learning techniques for liver and liver tumor segmentation: A review.
    Gul S; Khan MS; Bibi A; Khandakar A; Ayari MA; Chowdhury MEH
    Comput Biol Med; 2022 Aug; 147():105620. PubMed ID: 35667155
    [TBL] [Abstract][Full Text] [Related]  

  • 46. Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs.
    Tang F; Liang S; Zhong T; Huang X; Deng X; Zhang Y; Zhou L
    Eur Radiol; 2020 Feb; 30(2):823-832. PubMed ID: 31650265
    [TBL] [Abstract][Full Text] [Related]  

  • 47. 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]  

  • 48. Adaptive Fusion of Deep Learning With Statistical Anatomical Knowledge for Robust Patella Segmentation From CT Images.
    Zhao J; Jiang T; Lin Y; Chan LC; Chan PK; Wen C; Chen H
    IEEE J Biomed Health Inform; 2024 May; 28(5):2842-2853. PubMed ID: 38446653
    [TBL] [Abstract][Full Text] [Related]  

  • 49. Catheter segmentation in X-ray fluoroscopy using synthetic data and transfer learning with light U-nets.
    Gherardini M; Mazomenos E; Menciassi A; Stoyanov D
    Comput Methods Programs Biomed; 2020 Aug; 192():105420. PubMed ID: 32171151
    [TBL] [Abstract][Full Text] [Related]  

  • 50. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks.
    Tong N; Gou S; Yang S; Ruan D; Sheng K
    Med Phys; 2018 Oct; 45(10):4558-4567. PubMed ID: 30136285
    [TBL] [Abstract][Full Text] [Related]  

  • 51. Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation.
    Lee SB; Hong Y; Cho YJ; Jeong D; Lee J; Yoon SH; Lee S; Choi YH; Cheon JE
    Korean J Radiol; 2023 Apr; 24(4):294-304. PubMed ID: 36907592
    [TBL] [Abstract][Full Text] [Related]  

  • 52. Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation.
    Li X; Yu L; Chen H; Fu CW; Xing L; Heng PA
    IEEE Trans Neural Netw Learn Syst; 2021 Feb; 32(2):523-534. PubMed ID: 32479407
    [TBL] [Abstract][Full Text] [Related]  

  • 53. Technical Note: A deep learning-based autosegmentation of rectal tumors in MR images.
    Wang J; Lu J; Qin G; Shen L; Sun Y; Ying H; Zhang Z; Hu W
    Med Phys; 2018 Jun; 45(6):2560-2564. PubMed ID: 29663417
    [TBL] [Abstract][Full Text] [Related]  

  • 54. Efficient Combination of CNN and Transformer for Dual-Teacher Uncertainty-guided Semi-supervised Medical Image Segmentation.
    Xiao Z; Su Y; Deng Z; Zhang W
    Comput Methods Programs Biomed; 2022 Nov; 226():107099. PubMed ID: 36116398
    [TBL] [Abstract][Full Text] [Related]  

  • 55. Automatic Segmentation of Multiple Organs on 3D CT Images by Using Deep Learning Approaches.
    Zhou X
    Adv Exp Med Biol; 2020; 1213():135-147. PubMed ID: 32030668
    [TBL] [Abstract][Full Text] [Related]  

  • 56. Deep learning for automatic mandible segmentation on dental panoramic x-ray images.
    Machado LF; Watanabe PCA; Rodrigues GA; Junior LOM
    Biomed Phys Eng Express; 2023 Mar; 9(3):. PubMed ID: 36724498
    [TBL] [Abstract][Full Text] [Related]  

  • 57. 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]  

  • 58. Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework.
    Xia S; Li Q; Zhu HT; Zhang XY; Shi YJ; Yang D; Wu J; Guan Z; Lu Q; Li XT; Sun YS
    BMC Cancer; 2024 Mar; 24(1):315. PubMed ID: 38454349
    [TBL] [Abstract][Full Text] [Related]  

  • 59. Medical image diagnosis of prostate tumor based on PSP-Net+VGG16 deep learning network.
    Ye LY; Miao XY; Cai WS; Xu WJ
    Comput Methods Programs Biomed; 2022 Jun; 221():106770. PubMed ID: 35640389
    [TBL] [Abstract][Full Text] [Related]  

  • 60. Shading artifact correction in breast CT using an interleaved deep learning segmentation and maximum-likelihood polynomial fitting approach.
    Ghazi P; Hernandez AM; Abbey C; Yang K; Boone JM
    Med Phys; 2019 Aug; 46(8):3414-3430. PubMed ID: 31102462
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 7.