disease-detection

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disease-detection

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# Leveraging uncertainty information from deep neural networks for disease detection

Code and models for Christian Leibig, Vaneeda Allken, Murat Seckin Ayhan, Philipp Berens, Siegfried Wahl [(Scientific Reports, 2017)](https://www.nature.com/articles/s41598-017-17876-z) / [(preprint, 2016)](https://www.biorxiv.org/content/early/2017/10/18/084210), developed at the ZEISS Vision Science Lab in collaboration with the Berenslab @ University of Tuebingen.

## Getting started

If you want to use the Bayesian CNNs for detecting diabetic retinopathy with uncertainty have a look at `disease-detection/example.ipynb`.

To get things running you need a machine with a NVIDIA GPU and install [nvidia-docker](https://github.com/NVIDIA/nvidia-docker#quick-start). The docker image can be built as follows: Clone the repository and `cd` into the folder `disease-detection` and execute:

```bash
docker build -t uncertain-ai-diagnostics -f docker/Dockerfile .
```
Next, start a Docker container:
```bash
nvidia-docker run -it -p 8888:8888 uncertain-ai-diagnostics
```
This will fire up a jupyter notebook server and tell you the URL you have to point your browser to in order to play around with
the example notebook.

## Contact

leibig.christian@gmail.com

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