Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading

@article{Zhou2018MultiCellMC,
  title={Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading},
  author={K. Zhou and Zaiwang Gu and W. Liu and Weixin Luo and J. Cheng and Shenghua Gao and J. Liu},
  journal={2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
  year={2018},
  pages={2724-2727}
}
  • K. Zhou, Zaiwang Gu, +4 authors J. Liu
  • Published 2018
  • Computer Science, Engineering, Medicine
  • 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  • Diabetic Retinopathy (DR) is a non-negligible eye disease among patients with Diabetes Mellitus, and automatic retinal image analysis algorithm for the DR screening is in high demand. Considering the resolution of retinal image is very high, where small pathological tissues can be detected only with large resolution image and large local receptive field are required to identify those late stage disease, but directly training a neural network with very deep architecture and high resolution image… CONTINUE READING
    19 Citations
    Multi-scale Stepwise Training Strategy of Convolutional Neural Networks for Diabetic Retinopathy Severity Assessment
    • 1
    Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network
    • 1
    BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading
    • 10
    • PDF
    CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading
    • 20
    • PDF
    SUNet: A Lesion Regularized Model for Simultaneous Diabetic Retinopathy and Diabetic Macular Edema Grading
    • Z. Tu, Shenghua Gao, +6 authors J. Liu
    • Computer Science
    • 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
    • 2020
    • 1
    CABNet: Category Attention Block for Imbalanced Diabetic Retinopathy Grading
    • 1
    Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis
    • 2
    • PDF
    Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset
    • 13
    • PDF

    References

    SHOWING 1-10 OF 11 REFERENCES
    Classification of diabetic retinopathy images using multi-class multiple-instance learning based on color correlogram features
    • 33
    • PDF
    Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection
    • 81
    • Highly Influential
    • PDF
    Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning.
    • 332
    • PDF
    ImageNet classification with deep convolutional neural networks
    • 58,386
    • PDF
    Automated microaneurysm detection using local contrast normalization and local vessel detection
    • 299
    Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
    • 5,344
    • PDF
    A fully automated comparative microaneurysm digital detection system
    • 187
    • PDF
    ImageNet: A large-scale hierarchical image database
    • 14,761
    • PDF
    Inception-v4
    • inception-resnet and the impact of residual connections on learning. In AAAI, pages 4278–4284
    • 2017