This is the source code for the 3rd place solution to the Second National Data Science Bowl hosted by Kaggle.com. For documenation about the approach look here
I used the anaconda default distribution with all the libraries that came with it. Next to this I used opencv(cv2), pydicom and MxNet (20151228 but later version will most probably be fine). For more detailed windows 64 installation instructions look here.
The dicom data needs to be downloaded from Kaggle and must be extracted in the data_kaggle/train /validate and /test folders.
In the settings.py you can adjust some parameters. The most important one is the special "quick mode". This makes training the model 5x faster at the expense of some datascience rigor. Instead of training different folds to calibrate upon to prevent overfitting we train only one fold. This overfits a bit in step 3 and 4 but still results in a solid 0.0105 score which is enough for a 3rd place on the LB. Not choosing quick mode takes much longer to train but will result in less overfit and gives 0.0101 on the LB. Which is almost 2nd place and maybe with some luck it is.
The solution should be gentle on the GPU because of the small batchsize. Any recent GPU supported by MxNet should do the job I figure. The lowest card I tried (and that worked) was a GT740.