The model description can be found in ./documentation/DL_Munich_model_desc.pdf

• Ubuntu 14.04

• GPU: Nvidia GTX 1080
• CPU: Intel(R) Core(TM) i7-4930K CPU
• RAM: 32GB of RAM
• Around 200GB of free Memory

• opencv-python 3.2.0.6
• Python 3.4.3
• dicom 0.9.9-1
• joblib 0.10.3
• tensorflow-gpu 1.0.1
• SimpleITK 0.10.0.0
• numpy 1.12.0
• pandas 0.19.2
• scipy 0.18.1
• scikit-image 0.12.3
• scikit-learn 0.18.1

adjust raw_data_absolute_path in "params_niklas_fix.py" (line 6) to the raw dsb3 data directory. The raw dsb3 data directory is expected to contain the following folders and files:

• stage1/ (unzipped stage1.7z)
• stage2/ (unzipped stage2.7z)
• stage2_sample_submission.csv

adjust raw_LUNA_absolute_path in "params_niklas_fix.py" (line 7) to the raw LUNA data directory. The directory is expected to contain the following folders and files from the LUNA16 challenge (https://luna16.grand-challenge.org/data/):

• subset0.zip to subset9.zip: 10 zip files which contain all CT images
• annotations.csv: csv file that contains the annotations used as reference standard for the 'nodule detection' track
• sampleSubmission.csv: an example of a submission file in the correct format
• candidates_V2.csv: csv file that contains the candidate locations for the ‘false positive reduction’ track

The GPU ID and number of cores for multithreading can be adjusted in line 23,24 in "params_niklas_fix.py": ('n_CPUs', 4), ('GPU_ids', [0]),

Download the checkpoint folder from: https://www.dropbox.com/sh/70dvei9ie7fpwpa/AADTU8pc8T5TzII38j5kstroa?dl=0 and extract it to the ./ directory

The intermediate steps will produce outputs in the ./datapipeline_final/ directory. The final 2 submissions will be placed in the ./out/ directory.

$ sh run_pipeline.sh