Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands

October 23, 2019 · Entered Twilight · 🏛 arXiv.org

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Repo contents: .gitattributes, .gitignore, LICENSE, README.md, augmentations.py, best_border_resnet_paper.pt, best_border_unet_paper.pt, best_border_unode_paper.pt, dataloader.py, inference_utils.py, metrics.py, model_utils.py, models.py, networks.svg, results.png, results.svg, train_models.ipynb, train_utils.py

Authors Hans Pinckaers, Geert Litjens arXiv ID 1910.10470 Category eess.IV: Image & Video Processing Cross-listed cs.CV Citations 37 Venue arXiv.org Repository https://github.com/DIAGNijmegen/neural-odes-segmentation ⭐ 113 Last Checked 1 month ago

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.