hrshwrdhn / NAS-Lung

3D NAS for Pulmonary Nodules Classification

NAS-Lung

3D NAS for Pulmonary Nodules Classification

Jiang et al. Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification. (under review)

Architecture

Results

NASLung9

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
3D-NAS (ours)	88.71	85.85	91.17	87.20	7.99
NASLung9 (ours)	91.72	90.21	92.85	90.81	63.53

3D-NAS

Model	Accu.	Sens.	Spec.	F1 Score	para.
Model-A	88.71	85.85	91.17	87.20	7.99
Model-B	88.16	87.11	88.61	86.90	8.05
Model-C	88.46	83.83	92.15	86.68	8.05
Model-D	87.59	83.64	90.52	85.85	0.63
Model-E	88.27	82.41	92.92	86.17	7.79
Model-F	88.13	87.86	88.89	86.88	11.28
Model-G	88.61	85.41	91.40	87.02	11.33
Model-H	87.94	83.79	91.10	86.13	11.28
Model-I	88.11	86.08	89.73	86.64	11.33

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 http://pytorch.org 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, [e-mail]

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 Archivedoi:http://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX. URL https://wiki.cancerimagingarchive.net/display/Public/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.