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).
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Alain Jungo, Olivier Scheidegger, Mauricio Reyes, Fabian Balsiger: pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis (2020) arXiv
- Fernando Pérez-García, Rachel Sparks, Sebastien Ourselin: TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning (2020) arXiv
- Frank Mancolo: Eisen: a python package for solid deep learning (2020) arXiv