BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

1718 related articles for article (PubMed ID: 32277478)

  • 21. Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images.
    Naceur MB; Saouli R; Akil M; Kachouri R
    Comput Methods Programs Biomed; 2018 Nov; 166():39-49. PubMed ID: 30415717
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Segmenting brain tumors from FLAIR MRI using fully convolutional neural networks.
    Ribalta Lorenzo P; Nalepa J; Bobek-Billewicz B; Wawrzyniak P; Mrukwa G; Kawulok M; Ulrych P; Hayball MP
    Comput Methods Programs Biomed; 2019 Jul; 176():135-148. PubMed ID: 31200901
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Auto-Segmentation and Classification of Glioma Tumors with the Goals of Treatment Response Assessment Using Deep Learning Based on Magnetic Resonance Imaging.
    Papi Z; Fathi S; Dalvand F; Vali M; Yousefi A; Tabatabaei MH; Amouheidari A; Abedi I
    Neuroinformatics; 2023 Oct; 21(4):641-650. PubMed ID: 37458971
    [TBL] [Abstract][Full Text] [Related]  

  • 24. 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]  

  • 25. Deformable registration of magnetic resonance images using unsupervised deep learning in neuro-/radiation oncology.
    Osman AFI; Al-Mugren KS; Tamam NM; Shahine B
    Radiat Oncol; 2024 May; 19(1):61. PubMed ID: 38773620
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Deep semi-supervised learning for brain tumor classification.
    Ge C; Gu IY; Jakola AS; Yang J
    BMC Med Imaging; 2020 Jul; 20(1):87. PubMed ID: 32727476
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Evaluation of multislice inputs to convolutional neural networks for medical image segmentation.
    Vu MH; Grimbergen G; Nyholm T; Löfstedt T
    Med Phys; 2020 Dec; 47(12):6216-6231. PubMed ID: 33169365
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Deep learning-based convolutional neural network for intramodality brain MRI synthesis.
    Osman AFI; Tamam NM
    J Appl Clin Med Phys; 2022 Apr; 23(4):e13530. PubMed ID: 35044073
    [TBL] [Abstract][Full Text] [Related]  

  • 29. An Attention-Guided CNN Framework for Segmentation and Grading of Glioma Using 3D MRI Scans.
    Tripathi PC; Bag S
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(3):1890-1904. PubMed ID: 36350865
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Grading of gliomas using transfer learning on MRI images.
    Fasihi Shirehjini O; Babapour Mofrad F; Shahmohammadi M; Karami F
    MAGMA; 2023 Feb; 36(1):43-53. PubMed ID: 36326937
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Brain MR Image Classification for Glioma Tumor detection using Deep Convolutional Neural Network Features.
    Latif G; Iskandar DNFA; Alghazo J; Butt MM
    Curr Med Imaging; 2021; 17(1):56-63. PubMed ID: 32160848
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Glioma grading using a machine-learning framework based on optimized features obtained from T
    Sengupta A; Ramaniharan AK; Gupta RK; Agarwal S; Singh A
    J Magn Reson Imaging; 2019 Oct; 50(4):1295-1306. PubMed ID: 30895704
    [TBL] [Abstract][Full Text] [Related]  

  • 33. A Hybrid Approach Based on Deep CNN and Machine Learning Classifiers for the Tumor Segmentation and Classification in Brain MRI.
    Haq EU; Jianjun H; Huarong X; Li K; Weng L
    Comput Math Methods Med; 2022; 2022():6446680. PubMed ID: 36035291
    [TBL] [Abstract][Full Text] [Related]  

  • 34. 3D dense connectivity network with atrous convolutional feature pyramid for brain tumor segmentation in magnetic resonance imaging of human heads.
    Zhou Z; He Z; Shi M; Du J; Chen D
    Comput Biol Med; 2020 Jun; 121():103766. PubMed ID: 32568669
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Automatic upper airway segmentation in static and dynamic MRI via anatomy-guided convolutional neural networks.
    Xie L; Udupa JK; Tong Y; Torigian DA; Huang Z; Kogan RM; Wootton D; Choy KR; Sin S; Wagshul ME; Arens R
    Med Phys; 2022 Jan; 49(1):324-342. PubMed ID: 34773260
    [TBL] [Abstract][Full Text] [Related]  

  • 36. SDResU-Net: Separable and Dilated Residual U-Net for MRI Brain Tumor Segmentation.
    Zhang J; Lv X; Sun Q; Zhang Q; Wei X; Liu B
    Curr Med Imaging; 2020; 16(6):720-728. PubMed ID: 32723244
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Deep Convolutional Radiomic Features on Diffusion Tensor Images for Classification of Glioma Grades.
    Zhang Z; Xiao J; Wu S; Lv F; Gong J; Jiang L; Yu R; Luo T
    J Digit Imaging; 2020 Aug; 33(4):826-837. PubMed ID: 32040669
    [TBL] [Abstract][Full Text] [Related]  

  • 38. An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm.
    Wu W; Li D; Du J; Gao X; Gu W; Zhao F; Feng X; Yan H
    Comput Math Methods Med; 2020; 2020():6789306. PubMed ID: 32733596
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non-Fat-Sat Images and Tested on Fat-Sat Images.
    Zhang Y; Chan S; Park VY; Chang KT; Mehta S; Kim MJ; Combs FJ; Chang P; Chow D; Parajuli R; Mehta RS; Lin CY; Chien SH; Chen JH; Su MY
    Acad Radiol; 2022 Jan; 29 Suppl 1(Suppl 1):S135-S144. PubMed ID: 33317911
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi-atlas (MA) approach.
    Lavdas I; Glocker B; Kamnitsas K; Rueckert D; Mair H; Sandhu A; Taylor SA; Aboagye EO; Rockall AG
    Med Phys; 2017 Oct; 44(10):5210-5220. PubMed ID: 28756622
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 86.