Corpus ID: 218581873

Segmentation of Macular Edema Datasets with Small Residual 3D U-Net Architectures

@article{Frawley2020SegmentationOM,
  title={Segmentation of Macular Edema Datasets with Small Residual 3D U-Net Architectures},
  author={Jonathan Frawley and Chris G. Willcocks and Maged Habib and Caspar Geenen and David H. W. Steel and Boguslaw Obara},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.04697}
}
  • Jonathan Frawley, Chris G. Willcocks, +3 authors Boguslaw Obara
  • Published 2020
  • Computer Science, Engineering
  • ArXiv
  • This paper investigates the application of deep convolutional neural networks with prohibitively small datasets to the problem of macular edema segmentation. In particular, we investigate several different heavily regularized architectures. We find that, contrary to popular belief, neural architectures within this application setting are able to achieve close to human-level performance on unseen test images without requiring large numbers of training examples. Annotating these 3D datasets is… CONTINUE READING

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