# CheXNet-with-localization

**Repository Path**: cq_huangwei/CheXNet-with-localization

## Basic Information

- **Project Name**: CheXNet-with-localization
- **Description**: Weakly Supervised Learning for Findings Detection in Medical Images
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-11-22
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# CheXNet-with-localization
ADLxMLDS 2017 fall final

Team:XD

*黃晴 (R06922014), 王思傑 (R06922019), 曹爗文 (R06922022), 傅敏桓 (R06922030), 湯忠憲 (R06946003)*
## Weakly supervised localization :
In this task, we have to plot bounding boxes for each disease finding in a single chest X-ray without goundtruth (X, Y, width, height) in training set. The workflow is shown below:
### Workflow :
<img src="https://github.com/thtang/CheXNet-with-localization/blob/master/output/process_flow.png">
1) Predict findings
2) Use the classifier to plot heatmap (Grad-CAM)
3) Plot the bounding box base on Grad-CAM
### Package : 
`Pytorch==0.2.0` &nbsp; `torchvision==0.2.0` &nbsp;` matplotlib`  &nbsp;` scikit-image==0.13.1` &nbsp;` opencv_python==3.4.0.12` &nbsp;` numpy==1.13.3` &nbsp;`matplotlib==2.1.1` &nbsp;`scipy==1.0.0` &nbsp; `sklearn==0.19.1` &nbsp;

### Environment:
* OS: Linux
* Python 3.5
* GPU: 1080 Ti
* CPU: Xeon(R) E5-2667 v4
* RAM: 500 GB
### Experiments process:
1) preprocessing:
```
python3 preprocessing.py [path of images folder] [path to data_entry] [path to bbox_list_path] [path to train_txt] [path to valid_txt] [path of preprocessed output (folder)]
```

2) training:
```
python3 train.py [path of preprocessed output (folder)]
```

3) local testing:
```
python3 denseNet_localization.py [path to test.txt] [path of images folder]
```
4) Output txt format: <br>
After running denseNet_localization.py, you would get a txt file. The format is shown below:<br>
```
[image_path] [number_of_detection]
[disease] [x] [y] [width] [height]
[disease] [x] [y] [width] [height]
...
[image_path] [number_of_detection]
[disease] [x] [y] [width] [height]
[disease] [x] [y] [width] [height]
...
```
------
For DeepQ platform testing:

upload **deepQ_25.zip** to the platform. Then use following command:
```
python3 inference.py
```
------

5) For **visualization**, please refers to [issue](https://github.com/thtang/CheXNet-with-localization/issues/9). Credit to [Sadam1195](https://github.com/Sadam1195).

### Note :
In our .py script, I used the following script to assign the task running on GPU 0.<br>

```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
```
### Model :
<img src="https://github.com/thtang/CheXNet-with-localization/blob/master/output/multi_label_denseNet.png">
* Image is modified from Ref [2].

### Result :
*Prediction*<br>
<img src="https://github.com/thtang/CheXNet-with-localization/blob/master/output/prediction.png" width="320"><br>
*Heatmap per disease*
![Alt Text](https://github.com/thtang/CheXNet-with-localization/blob/master/output/heatmap_per_class.jpg)
Visualization of some heat maps with its ground-truth label (red) and its prediction
(blue) selected from each disease class. (From top-left to bottom: Atelectasis, Cardiomegaly,
Effusion, Infiltration, Mass, Nodule, Pneumonia and Pneumothorax)

*Bounding Box per patient*
![Alt Text](https://github.com/thtang/CheXNet-with-localization/blob/master/output/bb_select.JPG)
Visualization of some images with its ground-truth label (red) and its prediction
(blue) selected from each disease class.

**Refers to the [report](https://github.com/thtang/CheXNet-with-localization/blob/master/report.pdf) for more experiment results.**
## Reference:
1. *ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases* [[Arxiv]](https://arxiv.org/pdf/1705.02315.pdf)
2. *LEARNING TO DIAGNOSE FROM SCRATCH BY EXPLOITING DEPENDENCIES AMONG LABELS* [[Arxiv]](https://arxiv.org/pdf/1710.10501.pdf)
3. *CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning* [[Arxiv]](https://arxiv.org/pdf/1711.05225.pdf)
4. *Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization* [[Arxiv]](https://arxiv.org/pdf/1610.02391.pdf)

## Contact:
Feel free to contact me (thtang@nlg.csie.ntu.edu.tw) if you have any problem.