BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

197 related articles for article (PubMed ID: 33313521)

  • 1. A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting.
    Ghosal S; Zheng B; Chapman SC; Potgieter AB; Jordan DR; Wang X; Singh AK; Singh A; Hirafuji M; Ninomiya S; Ganapathysubramanian B; Sarkar S; Guo W
    Plant Phenomics; 2019; 2019():1525874. PubMed ID: 33313521
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Supervised and Weakly Supervised Deep Learning for Segmentation and Counting of Cotton Bolls Using Proximal Imagery.
    Adke S; Li C; Rasheed KM; Maier FW
    Sensors (Basel); 2022 May; 22(10):. PubMed ID: 35632096
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Aerial Imagery Analysis - Quantifying Appearance and Number of Sorghum Heads for Applications in Breeding and Agronomy.
    Guo W; Zheng B; Potgieter AB; Diot J; Watanabe K; Noshita K; Jordan DR; Wang X; Watson J; Ninomiya S; Chapman SC
    Front Plant Sci; 2018; 9():1544. PubMed ID: 30405675
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Sorghum Panicle Detection and Counting Using Unmanned Aerial System Images and Deep Learning.
    Lin Z; Guo W
    Front Plant Sci; 2020; 11():534853. PubMed ID: 32983210
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Active learning with point supervision for cost-effective panicle detection in cereal crops.
    Chandra AL; Desai SV; Balasubramanian VN; Ninomiya S; Guo W
    Plant Methods; 2020; 16():34. PubMed ID: 32161624
    [TBL] [Abstract][Full Text] [Related]  

  • 6. From Prototype to Inference: A Pipeline to Apply Deep Learning in Sorghum Panicle Detection.
    James C; Gu Y; Potgieter A; David E; Madec S; Guo W; Baret F; Eriksson A; Chapman S
    Plant Phenomics; 2023; 5():0017. PubMed ID: 37040294
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Weakly Supervised Learning of 3D Deep Network for Neuron Reconstruction.
    Huang Q; Chen Y; Liu S; Xu C; Cao T; Xu Y; Wang X; Rao G; Li A; Zeng S; Quan T
    Front Neuroanat; 2020; 14():38. PubMed ID: 32848636
    [TBL] [Abstract][Full Text] [Related]  

  • 8.
    Sadeghi-Tehran P; Virlet N; Ampe EM; Reyns P; Hawkesford MJ
    Front Plant Sci; 2019; 10():1176. PubMed ID: 31616456
    [TBL] [Abstract][Full Text] [Related]  

  • 9. DeepSeedling: deep convolutional network and Kalman filter for plant seedling detection and counting in the field.
    Jiang Y; Li C; Paterson AH; Robertson JS
    Plant Methods; 2019; 15():141. PubMed ID: 31768186
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Weakly supervised mitosis detection in breast histopathology images using concentric loss.
    Li C; Wang X; Liu W; Latecki LJ; Wang B; Huang J
    Med Image Anal; 2019 Apr; 53():165-178. PubMed ID: 30798116
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Robust Semi-Supervised Traffic Sign Recognition via Self-Training and Weakly-Supervised Learning.
    Nartey OT; Yang G; Asare SK; Wu J; Frempong LN
    Sensors (Basel); 2020 May; 20(9):. PubMed ID: 32397197
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Deep Count: Fruit Counting Based on Deep Simulated Learning.
    Rahnemoonfar M; Sheppard C
    Sensors (Basel); 2017 Apr; 17(4):. PubMed ID: 28425947
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Towards a Weakly Supervised Framework for 3D Point Cloud Object Detection and Annotation.
    Meng Q; Wang W; Zhou T; Shen J; Jia Y; Van Gool L
    IEEE Trans Pattern Anal Mach Intell; 2022 Aug; 44(8):4454-4468. PubMed ID: 33656990
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Automatic Detection and Counting of Wheat Spikelet Using Semi-Automatic Labeling and Deep Learning.
    Qiu R; He Y; Zhang M
    Front Plant Sci; 2022; 13():872555. PubMed ID: 35707612
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Utilization of a high-throughput shoot imaging system to examine the dynamic phenotypic responses of a C4 cereal crop plant to nitrogen and water deficiency over time.
    Neilson EH; Edwards AM; Blomstedt CK; Berger B; Møller BL; Gleadow RM
    J Exp Bot; 2015 Apr; 66(7):1817-32. PubMed ID: 25697789
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.
    Pang S; Yu Z; Orgun MA
    Comput Methods Programs Biomed; 2017 Mar; 140():283-293. PubMed ID: 28254085
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Using Convolutional Neural Networks to Efficiently Extract Immense Phenological Data From Community Science Images.
    Reeb RA; Aziz N; Lapp SM; Kitzes J; Heberling JM; Kuebbing SE
    Front Plant Sci; 2021; 12():787407. PubMed ID: 35111176
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions.
    Zhang R; Lin L; Wang G; Wang M; Zuo W
    IEEE Trans Pattern Anal Mach Intell; 2019 Mar; 41(3):596-610. PubMed ID: 29993474
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Wheat ear counting using K-means clustering segmentation and convolutional neural network.
    Xu X; Li H; Yin F; Xi L; Qiao H; Ma Z; Shen S; Jiang B; Ma X
    Plant Methods; 2020; 16():106. PubMed ID: 32782453
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.