This repository includes codes, models, and test results for our paper: "Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention". This project is licensed for non-commerical research purpose only.
Comparison of our SGDA and other multi-domain methods in terms of FROC on dataset LUNA16, tianchi, and russia. Values below the names of datasets are FROCs (unit: %). All the methods utilize NoduleNet as backbone: (1) shared models with the prefix 'uni-', (2) independent models with the word 'single' in the name, (3) multi-domain methods, (4) universal models with 'SG' in the name (Ours).
| Method | #Adapters | #Groups | #Params | LUNA16 | tianchi | russia | Avg | Pre-trained Model |
|---|---|---|---|---|---|---|---|---|
| single NoduleNet | - | - | 16.73Mx3 | 77.71 | 68.23 | 37.19 | 61.04 | model_luna16 model_tianchi model_russia & res_luna16 res_tianchi res_russia |
| uniNoduleNet | - | - | 39.50M | 79.88 | 68.60 | 33.35 | 60.61 | model & res_luna16 res_tianchi res_russia |
| NoduleNet+BN | 3 | - | 39.51M | 79.94 | 68.12 | 36.52 | 61.52 | model & res_luna16 res_tianchi res_russia |
| NoduleNet+series | 3 | - | 40.14M | 78.44 | 70.41 | 33.39 | 60.74 | model & res_luna16 res_tianchi res_russia |
| NoduleNet+parallel | 3 | - | 40.13M | 78.57 | 70.14 | 35.61 | 61.44 | model & res_luna16 res_tianchi res_russia |
| NoduleNet+separable | 3 | - | 34.68M | 66.31 | 62.26 | 32.96 | 53.84 | model & res_luna16 res_tianchi res_russia |
| NoduleNet+SNR | - | - | 39.50M | 69.52 | 66.57 | 36.76 | 57.61 | model & res_luna16 res_tianchi res_russia |
| single NoduleNet+SE | - | - | 16.74Mx3 | 77.78 | 68.86 | 38.06 | 61.56 | model_luna16 model_tianchi model_russia & res_luna16 res_tianchi res_russia |
| uniSENoduleNet | - | - | 39.51M | 80.53 | 69.13 | 34.34 | 61.33 | model & res_luna16 res_tianchi res_russia |
| NoduleNet+SE | 3 | - | 39.54M | 78.89 | 72.33 | 35.89 | 62.37 | model & res_luna16 res_tianchi res_russia |
| DANoduleNet | 3 | - | 39.54M | 82.63 | 73.29 | 38.50 | 64.80 | model & res_luna16 res_tianchi res_russia |
| single NoduleNet+SGSE | - | 4 | 16.77Mx3 | 78.30 | 70.36 | 39.01 | 62.55 | model_luna16 model_tianchi model_russia & res_luna16 res_tianchi res_russia |
| uniSGSENoduleNet | - | 4 | 39.54M | 81.12 | 71.00 | 38.42 | 63.51 | model & res_luna16 res_tianchi res_russia |
| NoduleNet+SGSE | 3 | 4 | 39.62M | 80.93 | 70.94 | 38.30 | 63.39 | model & res_luna16 res_tianchi res_russia |
| SGDANoduleNet | 3 | 4 | 39.82M | 81.91 | 77.13 | 37.15 | 65.39 | model & res_luna16 res_tianchi res_russia |
Comparison of our SGDA and other multi-domain methods in terms of FROC on dataset PN9. The values are pulmonary nodule detection sensitivities (unit: %) with each column representing the average number of false positives per CT image. All the methods utilizes SANet as backbone: (1) baseline model with the prefix 'uni-', (2) universal models with 'SG' in the name (Ours).
| Method | #Adapters | #Groups | #Params | 0.125 | 0.25 | 0.5 | 1.0 | 2.0 | 4.0 | 8.0 | Avg | Pre-trained Model |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| uniSANet | - | - | 15.28M | 38.08 | 45.05 | 54.46 | 64.50 | 75.33 | 83.86 | 89.96 | 64.46 | model & res |
| DASANet | 3 | - | 15.32M | 54.86 | 54.86 | 54.86 | 64.94 | 75.43 | 83.53 | 88.18 | 68.09 | model & res |
| *SGDASANet w/o CA | 3 | 4 | 15.36M | 52.06 | 52.06 | 58.63 | 66.33 | 77.05 | 85.13 | 90.12 | 68.77 | model & res |
| *SGDASANet w/ CA | 3 | 4 | 15.45M | 57.63 | 57.63 | 57.63 | 65.73 | 75.09 | 83.56 | 88.25 | 69.36 | model & res |
The code is built with the following libraries:
Besides, you need to install a custom module for bounding box NMS and overlap calculation.
cd build/box
python setup.py install
Pulmonary nodule datasets. 'Scans' denotes the number of CT scans. 'Nodules' denotes the number of labeled nodules. 'Class' denotes the class number. And 'Raw' means whether the dataset contains raw CT scans. 'Image Size' gives the dimensions of the CT image matrix alont the x, y, and z axes. 'Spacing' gives the voxel sizes (mm) along the x, y, and z axes.
| Dataset | Year | Scans | Nodules | Class | Raw | File Size | Image Size | Spacing | Source Link |
|---|---|---|---|---|---|---|---|---|---|
| LUNA16 | 2016 | 601 | 1186 | 2 | Yes | 25M-258M | 512x512x95-512x512x733 | (0.86,0.86,2.50)-(0.64,0.64,0.50) | link & split |
| tianchi | 2017 | 800 | 1244 | 2 | Yes | 26M-343M | 512x512x114-512x512x1034 | (0.66,0.66,2.50)-(0.69,0.69,0.30) | link & split |
| russia | 2018 | 364 | 1850 | 2 | Yes | 80M-491M | 512x512x313-512x512x1636 | (0.62,0.62,0.80)-(0.78,0.78,0.40) | link & split |
| PN9 | 2021 | 8796 | 40436 | 9 | No | 5.6M-73M | 212x212x181-455x455x744 | (1.00,1.00,1.00)-(1.00,1.00,1.00) | link & split |
Download the datasets and add the information to configs/*config*.py.
Please refer to specificFiles/LIDC/lung_seg.py and specificFiles/LIDC/preprocess.py for the data preprocessing.
Run the following scripts to evaluate the model and obtain the results of FROC analysis.
python universal_test_sanet.py --ckpt='./results/model/model.ckpt' --save_dir='./results/'
This implementation supports multi-gpu, data_parallel training.
Change training configuration and data configuration in configs/*config*.py, especially the path to preprocessed data.
Run the training script:
python SGDA_train_sanet_middle_top.py
If you are using the code/model/data provided here in a publication, please consider citing:
@article{SGDA22,
author={Rui Xu and Zhi Liu and Yong Luo and Han Hu and Li Shen and Bo Du and Kaiming Kuang and and Jiancheng Yang},
title={Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention},
journal={},
year={},
volume={},
number={},
pages={},
doi={}}
For any questions, please contact: rui.xu AT whu.edu.cn.