https://github.com/masilab/deeplungscreening
https://github.com/masilab/deeplungscreening
Last synced: 12 days ago
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- Host: GitHub
- URL: https://github.com/masilab/deeplungscreening
- Owner: MASILab
- Created: 2020-12-04T14:30:04.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2025-04-29T20:58:31.000Z (9 months ago)
- Last Synced: 2025-04-29T21:34:23.837Z (9 months ago)
- Language: Python
- Size: 63.8 MB
- Stars: 4
- Watchers: 3
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# DeepLungScreening Overview
This is the Deep Learning Open-source Lung cancer risk estimation repository, with PyTorch implementations. We prevoide source code, running tutorial, pre-trained model, docker, and [example image](https://drive.google.com/drive/folders/1gyKfWa_vmI_0K0dZ_lBUynCnOJhy3dKU?usp=sharing) in this repository.
Please see the requirement in the Dockerfile. Different version packages should be easy to adapt (not guarantee).
# Tutorial
All the steps can be find in run_all.sh. A INPUTS and OUTPUTS example can be found here.
Fig. 1. Step 1-3 are summarized in this Figure
## step 1: image pre-process
> python3 ./1_preprocess/step1_main.py --sess_csv ${SPLIT_CSV} --prep_root ${PREP_ROOT} --ori_root ${ORI_ROOT}
```${SPLIT_CSV}``` is the csv file stores the scan list (and meta data) you want to test.
```${PREP_ROOT}``` is the data folder saves the pre-processed result.
```${ORI_ROOT}``` is the data folder stores the scans you want to test.
## step 2: nodule detection
> python3 ./2_nodule_detection/step2_main.py --sess_csv ${SPLIT_CSV} --bbox_root ${BBOX_ROOT} --prep_root ${PREP_ROOT}
```${BBOX_ROOT}``` is the data folder stores the nodule detection results.
## step 3: feature extraction
> python3 ./3_feature_extraction/step3_main.py --sess_csv ${SPLIT_CSV} --bbox_root ${BBOX_ROOT} --prep_root ${PREP_ROOT} --feat_root ${FEAT_ROOT}
```${FEAT_ROOT}``` is the data folder stores the image feature results.
Fig. 2. Step 4 are summarized in this Figure
## step 4: co-learning of meta data and image
> python3 ./4_co_learning/step4_main.py --sess_csv ${SPLIT_CSV} --feat_root ${FEAT_ROOT} --save_csv_path ${PRED_CSV}
```${PRED_CSV}``` is the predicted result from deep learning method.
# Docker
The Dockerfile is shared in this repository. The docker image rg15/deeplung:0.3 can be downloaded from DockHub.