BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

174 related articles for article (PubMed ID: 38781811)

  • 21. A convolutional neural network algorithm for automatic segmentation of head and neck organs at risk using deep lifelong learning.
    Chan JW; Kearney V; Haaf S; Wu S; Bogdanov M; Reddick M; Dixit N; Sudhyadhom A; Chen J; Yom SS; Solberg TD
    Med Phys; 2019 May; 46(5):2204-2213. PubMed ID: 30887523
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Automatic Tumor Segmentation With a Convolutional Neural Network in Multiparametric MRI: Influence of Distortion Correction.
    Bielak L; Wiedenmann N; Nicolay NH; Lottner T; Fischer J; Bunea H; Grosu AL; Bock M
    Tomography; 2019 Sep; 5(3):292-299. PubMed ID: 31572790
    [TBL] [Abstract][Full Text] [Related]  

  • 23. VENet: Variational energy network for gland segmentation of pathological images and early gastric cancer diagnosis of whole slide images.
    Zhang S; Yuan Z; Zhou X; Wang H; Chen B; Wang Y
    Comput Methods Programs Biomed; 2024 Jun; 250():108178. PubMed ID: 38652995
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Weaving attention U-net: A novel hybrid CNN and attention-based method for organs-at-risk segmentation in head and neck CT images.
    Zhang Z; Zhao T; Gay H; Zhang W; Sun B
    Med Phys; 2021 Nov; 48(11):7052-7062. PubMed ID: 34655077
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Image generation by GAN and style transfer for agar plate image segmentation.
    Andreini P; Bonechi S; Bianchini M; Mecocci A; Scarselli F
    Comput Methods Programs Biomed; 2020 Feb; 184():105268. PubMed ID: 31891902
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Micro-Net: A unified model for segmentation of various objects in microscopy images.
    Raza SEA; Cheung L; Shaban M; Graham S; Epstein D; Pelengaris S; Khan M; Rajpoot NM
    Med Image Anal; 2019 Feb; 52():160-173. PubMed ID: 30580111
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Abdomen CT multi-organ segmentation using token-based MLP-Mixer.
    Pan S; Chang CW; Wang T; Wynne J; Hu M; Lei Y; Liu T; Patel P; Roper J; Yang X
    Med Phys; 2023 May; 50(5):3027-3038. PubMed ID: 36463516
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Head and neck multi-organ segmentation on dual-energy CT using dual pyramid convolutional neural networks.
    Wang T; Lei Y; Roper J; Ghavidel B; Beitler JJ; McDonald M; Curran WJ; Liu T; Yang X
    Phys Med Biol; 2021 May; 66(11):. PubMed ID: 33915524
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Neuro-fuzzy patch-wise R-CNN for multiple sclerosis segmentation.
    Essa E; Aldesouky D; Hussein SE; Rashad MZ
    Med Biol Eng Comput; 2020 Sep; 58(9):2161-2175. PubMed ID: 32681214
    [TBL] [Abstract][Full Text] [Related]  

  • 30. SwinCross: Cross-modal Swin transformer for head-and-neck tumor segmentation in PET/CT images.
    Li GY; Chen J; Jang SI; Gong K; Li Q
    Med Phys; 2024 Mar; 51(3):2096-2107. PubMed ID: 37776263
    [TBL] [Abstract][Full Text] [Related]  

  • 31. CNN-based fully automatic wrist cartilage volume quantification in MR images: A comparative analysis between different CNN architectures.
    Vladimirov N; Brui E; Levchuk A; Al-Haidri W; Fokin V; Efimtcev A; Bendahan D
    Magn Reson Med; 2023 Aug; 90(2):737-751. PubMed ID: 37094028
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Cascaded deep learning-based auto-segmentation for head and neck cancer patients: Organs at risk on T2-weighted magnetic resonance imaging.
    Korte JC; Hardcastle N; Ng SP; Clark B; Kron T; Jackson P
    Med Phys; 2021 Dec; 48(12):7757-7772. PubMed ID: 34676555
    [TBL] [Abstract][Full Text] [Related]  

  • 33. 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]  

  • 34. BUS-Set: A benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets.
    Thomas C; Byra M; Marti R; Yap MH; Zwiggelaar R
    Med Phys; 2023 May; 50(5):3223-3243. PubMed ID: 36794706
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Information fusion for fully automated segmentation of head and neck tumors from PET and CT images.
    Shiri I; Amini M; Yousefirizi F; Vafaei Sadr A; Hajianfar G; Salimi Y; Mansouri Z; Jenabi E; Maghsudi M; Mainta I; Becker M; Rahmim A; Zaidi H
    Med Phys; 2024 Jan; 51(1):319-333. PubMed ID: 37475591
    [TBL] [Abstract][Full Text] [Related]  

  • 36. A deep learning based framework for accurate segmentation of cervical cytoplasm and nuclei.
    Song Y; Zhang L; Chen S; Ni D; Li B; Zhou Y; Lei B; Wang T
    Annu Int Conf IEEE Eng Med Biol Soc; 2014; 2014():2903-6. PubMed ID: 25570598
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Automated segmentation of deep brain nuclei using convolutional neural networks and susceptibility weighted imaging.
    Beliveau V; Nørgaard M; Birkl C; Seppi K; Scherfler C
    Hum Brain Mapp; 2021 Oct; 42(15):4809-4822. PubMed ID: 34322940
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Training of head and neck segmentation networks with shape prior on small datasets.
    Tappeiner E; Pröll S; Fritscher K; Welk M; Schubert R
    Int J Comput Assist Radiol Surg; 2020 Sep; 15(9):1417-1425. PubMed ID: 32556921
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Comparison of semi-automatic and manual segmentation methods for tumor delineation on head and neck squamous cell carcinoma (HNSCC) positron emission tomography (PET) images.
    Philip MM; Watts J; Moeini SNM; Musheb M; McKiddie F; Welch A; Nath M
    Phys Med Biol; 2024 Apr; 69(9):. PubMed ID: 38530298
    [No Abstract]   [Full Text] [Related]  

  • 40. MADR-Net: multi-level attention dilated residual neural network for segmentation of medical images.
    Balraj K; Ramteke M; Mittal S; Bhargava R; Rathore AS
    Sci Rep; 2024 Jun; 14(1):12699. PubMed ID: 38830932
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 9.