Deep learning project
Detecting and classifying malignant lung nodules from CT scans using PyTorch
Refer to the project report for the full description of the project.
This project is based on the book Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann.
Section of a CT scan
Sketch of the overall pipeline
Project tree description
In the project directory you can find:
Training_models.ipynb
: notebook containing scripts to generate some of the figures in the report and to train/test the models, as well as to plot metrics with TensorBoard.requirements.txt
: requirements to create a VirtualEnv environmentdownload_dataset.sh
: script to download the datasets (120 GB) from the Internetnotes.md
: contains some logging messages from the training/testing of the models
The code can be found under the following directories:
seg
: code to train the segmentation modelcls
: code to train the nodule-nonnodule classifier and the malignancy classifierutil
: utility functions (e.g. logging, transformations, augmentation, cache...)
Data can be found/downloaded in the directories:
data/part2/luna/
: csv files containing annotated data about nodules and CT scansdata/part2/models/
: Pre-trained models (best models obtained during the trials)data-unversioned/part2/luna/
: LUNA dataset (120 GB) which have to be downloaded from Internetdata-unversioned/part2/models/
: Trained models which have been saved (both best models and checkpoints)
Other directories:
runs/
: Tensorboard data