https://github.com/wangleihitcs/thoraxdiseaseclassification
A multi-label-classification model for common thorax disease.
https://github.com/wangleihitcs/thoraxdiseaseclassification
chest-xray-images multi-label-classification multi-label-image-classification nih-chest-x-ray thorax-disease-classification
Last synced: about 1 year ago
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A multi-label-classification model for common thorax disease.
- Host: GitHub
- URL: https://github.com/wangleihitcs/thoraxdiseaseclassification
- Owner: wangleihitcs
- Created: 2018-12-14T17:32:46.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-12-17T06:47:12.000Z (over 7 years ago)
- Last Synced: 2025-04-05T01:51:14.607Z (about 1 year ago)
- Topics: chest-xray-images, multi-label-classification, multi-label-image-classification, nih-chest-x-ray, thorax-disease-classification
- Language: Python
- Homepage:
- Size: 2.22 MB
- Stars: 12
- Watchers: 0
- Forks: 4
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
## Intro
A multi-label-classification model for chest diseases.
### Config
- python 2.7.15
- tensorflow 1.8.0
- python package
* nltk
* PIL
* json
* numpy
It is all of common tookits, so I don't give their links.
### DataDownload and Prepare
- NIH Chest X-ray Dataset[(kaggle's download link)](https://www.kaggle.com/nih-chest-xrays/data)
* you need copy 'Data_Entry_2017.csv' to dir 'data/'
* you need unzip 'images_001.zip' - 'images_012.zip' to 'data/images'
* you need copy 'train_val_list.txt' and 'test_list.txt' to 'data/'
- Pretrain VGG19 model
* you need to download [vgg_19_2016_08_28.tar.gz](http://download.tensorflow.org/models/vgg_19_2016_08_28.tar.gz)
* then extract it, copy 'vgg_19.ckpt' to 'data/pretrain_vgg/'
### Train
#### First, preprocess data
- get 'data_entry.json' and 'data_label.json'
```shell
$ cd preprocess
$ python get_data_entry.py
```
- get 'data/tfrecord/train-xx.tfrecord', 'data/tfrecord/test-xx.tfrecord', 'train_tfrecord_name.txt' and 'test_tfrecord_name.txt'
```shell
$ python datasets.py
```
#### Second, let's go train
- you can check mlc_model.py to ensure accuracy
```shell
$ python main.py
```
### Test Demo
I will release a demo.py, you can use it to test.
- you could provide Chest CT image to test
```shell
$ python demo.py --img='data/examples/CXR3_IM-1384-1001.png'
```
- test demo example
### Experiments
#### Loss
At last, I trained 100 epoch and the train mlc_loss_weighted reduce to 0.0455, it wasted 36 hours. You can see detials in 'data/log.txt'.
#### AUC
When epoch = 20, iter = 28000, I eval the auc. Actually, when epoch > 15, the model is overfitting, so you don't need trian too many epoch.
| | Ours | Paper | test num |
| :--- | :---: | :---: | :---: |
| Effusion | 0.7584 | 0.700 | 4658 |
| Pneumothorax | 0.7498 | 0.799 | 2665 |
| Edema | 0.7635 | 0.805 | 925 |
| Cardiomegaly | 0.7735 | 0.810 | 1069 |
| Pleural_Thickening | 0.7602 | 0.684 | 1143 |
| Atelectasis | 0.7532 | 0.700 | 3279 |
| Consolidation | 0.7399 | 0.703 | 1815 |
| Emphysema | 0.7385 | 0.833 | 1093 |
| Pneumonia | 0.7367 | 0.658 | 555 |
| Nodule | 0.7272 | 0.668 | 1623 |
| Mass | 0.7217 | 0.693 | 1748 |
| Infiltration | 0.7399 | 0.661 | 6112 |
| Hernia | 0.7520 | 0.871 | 86 |
| No Finding | 0.7782 | - | 9861 |
| Fibrosis | 0.7813 | 0.786 | 435 |
| Mean | 0.7516 | 0.745 | - |
### References
- Wang, Xiaosong, et al. **"Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases."** Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on. IEEE, 2017.
- Wang, Xiaosong, et al. **"Tienet: Text-image embedding network for common thorax disease classification and reporting in chest x-rays."** Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.