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

arXiv: https://arxiv.org/abs/1711.06853 · HTML: https://mlsanity.com/paper/1711.06853/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2017.

## Built on by (most cited first, 11 of 11)

- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging](https://mlsanity.com/paper/2103.01006/) (2021)
- [Learning from partially labeled data for multi-organ and tumor segmentation](https://mlsanity.com/paper/2211.06894/) (2022)
- [PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation](https://mlsanity.com/paper/2208.09350/) (2022)
- [medigan: a Python library of pretrained generative models for medical image synthesis](https://mlsanity.com/paper/2209.14472/) (2022)
- [Ivy: Templated Deep Learning for Inter-Framework Portability](https://mlsanity.com/paper/2102.02886/) (2021)
- [Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation](https://mlsanity.com/paper/2106.01596/) (2021)
- [MIP Candy: A Modular PyTorch Framework for Medical Image Processing](https://mlsanity.com/paper/2602.21033/) (2026)
- [Adaptive Contrastive Learning with Dynamic Correlation for Multi-Phase Organ Segmentation](https://mlsanity.com/paper/2210.08652/) (2022)
- [spectrai: A deep learning framework for spectral data](https://mlsanity.com/paper/2108.07595/) (2021)
- [An overview of deep learning in medical imaging](https://mlsanity.com/paper/2202.08546/) (2022)

## Abstract (arXiv)

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).