swMATH ID: 32337
Software Authors: Nick Pawlowski, Sofia Ira Ktena, Matthew C.H. Lee, Bernhard Kainz, Daniel Rueckert, Ben Glocker, Martin Rajchl
Description: 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 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).
Homepage: https://dltk.github.io
Source Code:  https://github.com/DLTK/DLTK
Related Software: TensorFlow; NiftyNet; PyTorch; TorchIO; Python; batchgenerators; medicaltorch; ImageNet; SciPy; SimpleITK; Mimicry; TF-GAN; pygan; Imaginaire; Keras-GAN; Keras; RadImageNet; nnDetection; MONAI; CleanX
Cited in: 0 Publications