/NAS-Lung

3D NAS for Pulmonary Nodules Classification, PR 2021

Primary LanguagePythonMIT LicenseMIT

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

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

Model Result

Training/Test Tips

  • Best practice for training and testing your models.
  • Feel free to ask any questions about coding. Fuhao Shen, 1048532267sfh@gmail.com

Acknowledgement

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.