fei-aiart / nas-lung

3D NAS for Pulmonary Nodules Classification, PR 2021

NAS-Lung

3D Neural Architecture Search (NAS) for Pulmonary Nodules Classification

Hanliang Jiang, Fuhao Shen, Fei Gao*, Weidong Han. Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification. Pattern Recognition, 113: 107825, 2021.

@article{Jiang2021naslung,
author = {Hanliang Jiang and Fuhao Shen and Fei Gao and Weidong Han},
title = {Learning efficient, explainable and discriminative representations for pulmonary nodules classification},
journal = {Pattern Recognition},
volume = {113},
pages = {107825},
year = {2021},
issn = {0031-3203},
doi = {https://doi.org/10.1016/j.patcog.2021.107825},
}

[Paper@PR] [Paper@arxiv] [Code@Github]

Architecture

Results

NASLung

model	Accu.	Sens.	Spec.	F1 Score	para.(M)
Multi-crop CNN	87.14	-	-	-	-
Nodule-level 2D CNN	87.30	88.50	86.00	87.23	-
Vanilla 3D CNN	87.40	89.40	85.20	87.25	-
DeepLung	90.44	81.42	-	-	141.57
AE-DPN	90.24	92.04	88.94	90.45	678.69
NASLung (ours)	90.77	85.37	95.04	89.29	16.84

Searched 3D Networks

Model	Accu.	Sens.	Spec.	F1 Score	para.
Model-1	88.83	87.20	90.12	87.50	0.14
Model-2	88.42	84.38	91.46	86.67	2.61
Model-3	88.17	84.44	91.60	86.50	3.90
Model-4	88.13	83.20	92.28	86.30	2.54
Model-5	87.97	83.72	91.31	86.22	0.43
Model-6	87.77	83.67	91.00	86.03	0.22
Model-7	87.76	84.14	89.79	85.98	0.86
Model-8	88.00	82.43	92.69	85.97	4.02
Model-9	88.04	78.01	96.09	85.36	4.06
Model-10	87.22	82.70	90.92	85.32	0.24

Prerequisites

• Linux or similar environment
• Python 3.7
• Pytorch 0.4.1
• NVIDIA GPU + CUDA CuDNN

Getting Started

Installation

• Clone this repo:

  git clone https://github.com/fei-hdu/NAS-Lung
  cd NAS-Lung

• Install PyTorch 0.4+ and torchvision from Pytorch and other dependencies (e.g., visdom and dominate). You can install all the dependencies by

  pip install -r requirments.txt

• Download Dataset LIDC-IDRI

Neural Architecture Search

python search_main.py --train_data_path {train_data_path}  --test_data_path {test_data_path} --save_module_path {save_module_path}

Train/Test

• Train a model

  sh run_training.sh

• Test a model

  python test.py --test_data_path {test_data_path} --preprocess_path {preprocess_path} --model_path {model_path}

DataSet

• LIDC-IDRI

Model Result

• our final result can be download:Google Drive

Training/Test Tips

• Best practice for training and testing your models.
• Feel free to ask any questions about coding. Fuhao Shen, [email protected]

Acknowledgement

• Our work/code is inspired by Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search, CVPR 2019.

Selected References

• S. Armato III, G. et al., Data from LIDC-IDRI, The Cancer Imaging . LIDC-IDRI.
• X. Li, Y. Zhou, Z. Pan, J. Feng, Partial order pruning: For best speed/accuracy trade-off in neural architecture search (2019) 9145–9153.
• S. Woo, J. Park, J.-Y. Lee, I. So Kweon, CBAM: Convolutional block attention module, in: Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 3–19.
• W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, L. Song, Sphereface: Deep hypersphere embedding for face recognition, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
• T. Elsken, J. H. Metzen, F. Hutter, Neural architecture search: A survey, Journal of Machine Learning Research 20 (55) (2019) 1–21.
• W. Zhu, C. Liu, W. Fan, X. Xie, Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification, in: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, 2018, pp. 673–681.

nas-lung's Issues

About figure

Thank you for your sharing. Could you tell me how to draw Figure 5 (Class Activation Diagram) in your paper? Can you provide some code? Thank you very much.

How to execute your codes? Where can I get your paper? Thanks!

Hi, fei.
Thanks for sharing your great work! I have some questions to ask you.

How to execute your codes? What is the order of executing your codes?
Where can I get your paper? If I use your code which paper should be referenced?

Thank you very much!
Best regards,
Chi Jingqian.

about test.py

hello,i want to ask about the param " --preprocess_path" ? python test.py --test_data_path {test_data_path} --preprocess_path {preprocess_path} --model_path {model_path} 。 And what "/data/xxx/LUNA/cls/crop_v3"?

Reproducing the preprocess folder

Hi,
Congrats on the amazing works and thanks for sharing the code.
I am also trying to run your code again, I have the LUNA16 dataset on my machine but I am wondering how can I reproduce the preprocess/lunaall and crop_v3 folders, are there any scripts that I can run?

Thank you so much

How to get this paper?

Hi, happy new year!
I'm very interested in your code. Would you tell me when the paper will be published. Thank you!

preprocess the files

Congrats on the amazing works and thanks for sharing the code.
I am also trying to run your code again, I have the LUNA16 dataset on my machine but I am wondering how can I reproduce the preprocess/lunaall and crop_v3 folders, are there any scripts that I can run?

Thank you so much