BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

115 related articles for article (PubMed ID: 38422543)

  • 1. Sliding transformer with uncertainty estimation for vestibular schwannoma automatic segmentation.
    Liu Y; Li M; Li M; Wang X; Liang J; Chen G; Feng Y; Chen Z
    Phys Med Biol; 2024 Mar; 69(7):. PubMed ID: 38422543
    [No Abstract]   [Full Text] [Related]  

  • 2. Joint Vestibular Schwannoma Enlargement Prediction and Segmentation Using a Deep Multi-task Model.
    Wang K; George-Jones NA; Chen L; Hunter JB; Wang J
    Laryngoscope; 2023 Oct; 133(10):2754-2760. PubMed ID: 36495306
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Combining analysis of multi-parametric MR images into a convolutional neural network: Precise target delineation for vestibular schwannoma treatment planning.
    Lee WK; Wu CC; Lee CC; Lu CF; Yang HC; Huang TH; Lin CY; Chung WY; Wang PS; Wu HM; Guo WY; Wu YT
    Artif Intell Med; 2020 Jul; 107():101911. PubMed ID: 32828450
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Uncertainty-guided transformer for brain tumor segmentation.
    Chen Z; Peng C; Guo W; Xie L; Wang S; Zhuge Q; Wen C; Feng Y
    Med Biol Eng Comput; 2023 Dec; 61(12):3289-3301. PubMed ID: 37665558
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Deep learning for automatic segmentation of vestibular schwannoma: a retrospective study from multi-center routine MRI.
    Kujawa A; Dorent R; Connor S; Thomson S; Ivory M; Vahedi A; Guilhem E; Wijethilake N; Bradford R; Kitchen N; Bisdas S; Ourselin S; Vercauteren T; Shapey J
    Front Comput Neurosci; 2024; 18():1365727. PubMed ID: 38784680
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Automatic uncertainty-based quality controlled T1 mapping and ECV analysis from native and post-contrast cardiac T1 mapping images using Bayesian vision transformer.
    Arega TW; Bricq S; Legrand F; Jacquier A; Lalande A; Meriaudeau F
    Med Image Anal; 2023 May; 86():102773. PubMed ID: 36827870
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI.
    McGrath H; Li P; Dorent R; Bradford R; Saeed S; Bisdas S; Ourselin S; Shapey J; Vercauteren T
    Int J Comput Assist Radiol Surg; 2020 Sep; 15(9):1445-1455. PubMed ID: 32676869
    [TBL] [Abstract][Full Text] [Related]  

  • 9. nnUnetFormer: an automatic method based on nnUnet and transformer for brain tumor segmentation with multimodal MR images.
    Guo S; Chen Q; Wang L; Wang L; Zhu Y
    Phys Med Biol; 2023 Dec; 68(24):. PubMed ID: 37963410
    [No Abstract]   [Full Text] [Related]  

  • 10. Segmentation of Vestibular Schwannomas on Postoperative Gadolinium-Enhanced T1-Weighted and Noncontrast T2-Weighted Magnetic Resonance Imaging Using Deep Learning.
    Yao P; Shavit SS; Shin J; Selesnick S; Phillips CD; Strauss SB
    Otol Neurotol; 2022 Dec; 43(10):1227-1239. PubMed ID: 36240735
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Automated, fast, robust brain extraction on contrast-enhanced T1-weighted MRI in presence of brain tumors: an optimized model based on multi-center datasets.
    Teng Y; Chen C; Shu X; Zhao F; Zhang L; Xu J
    Eur Radiol; 2024 Feb; 34(2):1190-1199. PubMed ID: 37615767
    [TBL] [Abstract][Full Text] [Related]  

  • 12. An External Validation Study for Automated Segmentation of Vestibular Schwannoma.
    Suresh K; Luo G; Bartholomew RA; Brown A; Juliano AF; Lee DJ; Welling DB; Cai W; Crowson MG
    Otol Neurotol; 2024 Mar; 45(3):e193-e197. PubMed ID: 38361299
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Brain tumor segmentation using holistically nested neural networks in MRI images.
    Zhuge Y; Krauze AV; Ning H; Cheng JY; Arora BC; Camphausen K; Miller RW
    Med Phys; 2017 Oct; 44(10):5234-5243. PubMed ID: 28736864
    [TBL] [Abstract][Full Text] [Related]  

  • 14. An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced T1-weighted and high-resolution T2-weighted MRI.
    Shapey J; Wang G; Dorent R; Dimitriadis A; Li W; Paddick I; Kitchen N; Bisdas S; Saeed SR; Ourselin S; Bradford R; Vercauteren T
    J Neurosurg; 2019 Dec; 134(1):171-179. PubMed ID: 31812137
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Automated segmentation of prostate zonal anatomy on T2-weighted (T2W) and apparent diffusion coefficient (ADC) map MR images using U-Nets.
    Zabihollahy F; Schieda N; Krishna Jeyaraj S; Ukwatta E
    Med Phys; 2019 Jul; 46(7):3078-3090. PubMed ID: 31002381
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm.
    Shapey J; Kujawa A; Dorent R; Wang G; Dimitriadis A; Grishchuk D; Paddick I; Kitchen N; Bradford R; Saeed SR; Bisdas S; Ourselin S; Vercauteren T
    Sci Data; 2021 Oct; 8(1):286. PubMed ID: 34711849
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Clinical Evaluation of a Multiparametric Deep Learning Model for Glioblastoma Segmentation Using Heterogeneous Magnetic Resonance Imaging Data From Clinical Routine.
    Perkuhn M; Stavrinou P; Thiele F; Shakirin G; Mohan M; Garmpis D; Kabbasch C; Borggrefe J
    Invest Radiol; 2018 Nov; 53(11):647-654. PubMed ID: 29863600
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Automatic segmentation of vestibular schwannomas from T1-weighted MRI with a deep neural network.
    Wang H; Qu T; Bernstein K; Barbee D; Kondziolka D
    Radiat Oncol; 2023 May; 18(1):78. PubMed ID: 37158968
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Quantifying U-Net uncertainty in multi-parametric MRI-based glioma segmentation by spherical image projection.
    Yang Z; Lafata K; Vaios E; Hu Z; Mullikin T; Yin FF; Wang C
    Med Phys; 2024 Mar; 51(3):1931-1943. PubMed ID: 37696029
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.