This is supplementary material for the manuscript:
"Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks" M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe and S. Mougiakakou IEEE Journal of Biomedical and Health infomatics (2018) https://arxiv.org/abs/1803.06167
In case of any questions, please do not hesitate to contact us.
A Dockerfile
is provided with all the necessary environment configurations. In order to build and run it you may use the following commands:
docker build -t lungnetenv .
docker run --name LungNet -it --rm -v "$PWD":/home lungnetenv /bin/bash
Some notes:
- the
--name
sets a name for the container for identification reasons. - the
-it
flag denotes that the container will be interactive. - the
--rm
flags means that when the container is stopped the container will also be deleted (i.e. docker start LungNet cannot be used). - the
-v
flag mounts the $PWD (current) directory of the host machine in the/home
of the container (guest).
In order for GPU support the flag --runtime=nvidia
can be used. For older versions of nvidia drivers the nvidia-docker
should be used.
After the successful excecution of the docker build
and docker run
commands, a bash promt from within the docker container will be available.
There are three scripts with a __main__
method:
get_fmd_db.py
: This script will download a test database and generate a training and validation datasets. These will be saved in.npz
formattrain.py
: This script will generate a model and train it using the two data files (fmd-train.npz
,fmd-val.npz
) generated from theget_fmd_db.py
test.py
: This script loads a model and passes a sample through. (Note: the directory of the model and weights should be defined in the file beforehand)
Using the bash promt of the container these commands could be used:
#/ python get_fmd_db.py
#/ python train.py
#/ python test.py
Important Note: The demo uses the Flickr Material database for demontration reasons, no particular efforts were made for the optimization of the network for this task.
The execution generates a folder for each run, which contains a .png
file with the architecute of the CNN a log file with the metrics that were used along with the best snapshots of the model while training. The training loss and accuracy are also shown during training.
Copyright (C) 2018 Marios Anthimopoulos, Stergios Christodoulidis, Stavroula Mougiakakou / University of Bern
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.