DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images

November 18, 2017 · Entered Twilight · 🏛 arXiv.org

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Repo contents: .gitignore, .travis.yml, CHANGELOG.md, LICENSE, MANIFEST.in, README.md, data, dltk, docs, examples, logo.png, requirements.txt, setup.cfg, setup.py, tests

Authors Nick Pawlowski, Sofia Ira Ktena, Matthew C. H. Lee, Bernhard Kainz, Daniel Rueckert, Ben Glocker, Martin Rajchl arXiv ID 1711.06853 Category cs.CV: Computer Vision Cross-listed cs.LG Citations 84 Venue arXiv.org Repository https://github.com/DLTK/DLTK ⭐ 1451 Last Checked 1 month ago

Abstract

We present DLTK, a toolkit providing baseline implementations for efficient experimentation with deep learning methods on biomedical images. It builds on top of TensorFlow and its high modularity and easy-to-use examples allow for a low-threshold access to state-of-the-art implementations for typical medical imaging problems. A comparison of DLTK's reference implementations of popular network architectures for image segmentation demonstrates new top performance on the publicly available challenge data "Multi-Atlas Labeling Beyond the Cranial Vault". The average test Dice similarity coefficient of $81.5$ exceeds the previously best performing CNN ($75.7$) and the accuracy of the challenge winning method ($79.0$).