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In March 2017, we participated to the third Data Science Bowl challenge organized by Kaggle.This year, the goal was to predict whether a high-riskpatient will be diagnosed with lung cancer within one year, based only on a low-dose CT scan.Our method, which combines deep neural networks and boosted trees, achieved the 10th place on the final leaderboard, over ~2000 teams.We developed two methods, both based on nodule detection using the LUNA dataset.Both methods pre-process the images to get a fixed 1mm x 1mm x 1mm resolution and segment the lungs using thresholding, morphological operations and connected components selection.Each U-net takes a stack of 11 slices as input and outputs a nodule segmentation mask of the 6th slice.It permits to take into account 3D information and improves the dice score from 0.50 for a single input (6th slice) to 0.75 for the 3D stack. If you have the error Import Error: cannot import name _cmatrices try $sudo python develop To run the script, you will have to run the 6 commands below.The 2 first generate the train features from the Pierre's and Simon's model, the 2 following generate the test features, the 5th train the XGboost model on the train features, and the 6th apply it on the test and save the csv submission file.

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