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 (2016) developed at the ZEISS Vision Science Lab in collaboration with the Berenslab.

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. The docker image can be built as follows: Clone the repository and cd into the folder disease-detection and execute:

docker build -t uncertain-ai-diagnostics -f docker/Dockerfile .

Next, start a Docker container:

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

[email protected]