Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands

Hans Pinckaers, Geert Litjens
10/23/2019
eess.IVcs.CV

Official code: DIAGNijmegen/neural-odes-segmentation (111 stars, MIT, last commit 2020-04-09).

Abstract

Automated medical image segmentation plays a key role in quantitative research and diagnostics. Convolutional neural networks based on the U-Net architecture are the state-of-the-art. A key disadvantage is the hard-coding of the receptive field size, which requires architecture optimization for each segmentation task. Furthermore, increasing the receptive field results in an increasing number of weights. Recently, Neural Ordinary Differential Equations (NODE) have been proposed, a new type of continuous depth deep neural network. This framework allows for a dynamic receptive field at a fixed memory cost and a smaller amount of parameters. We show on a colon gland segmentation dataset (GlaS) that these NODEs can be used within the U-Net framework to improve segmentation results while reducing memory load and parameter counts.

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Code Implementations(1)

DIAGNijmegen/neural-odes-segmentationOfficial100%
MIT

Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands

11128Python, Jupyter NotebookOct 21, 20196 years agoMIT

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Cite this paper

@article{pinckaers2019neural,
  title  = {Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands},
  author = {Hans Pinckaers and Geert Litjens},
  year   = {2019},
  eprint = {1910.10470},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1910.10470}
}

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