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

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 typica...

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Bibliographic Details
Published inarXiv.org
Main Authors Pawlowski, Nick, Ktena, Sofia Ira, Lee, Matthew C H, Kainz, Bernhard, Rueckert, Daniel, Glocker, Ben, Rajchl, Martin
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 18.11.2017
Subjects
Deep learning
Experimentation
Image segmentation
Medical imaging
Modularity
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Summary: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 (\(79.0\)).
ISSN:2331-8422