Predict-Lung-Disease

Author: yht

_posts/README.md

@@ -0,0 +1,74 @@
+# Predict-Lung-Disease-through-Chest-X-Ray
+We obtain this repository by refactoring the [code](https://github.com/Azure/AzureChestXRay) for the blog post [Using Microsoft AI to Build a Lung-Disease Prediction Model using Chest X-Ray Images](https://blogs.technet.microsoft.com/machinelearning/2018/03/07/using-microsoft-ai-to-build-a-lung-disease-prediction-model-using-chest-x-ray-images/). This instruction aims to help newcomers build the system in a very short time.   
+# Installation
+1. Clone this repository
+   ```Shell
+   git clone https://github.com/svishwa/crowdcount-mcnn.git
+   ```
+   We'll call the directory that you cloned PredictLungDisease `ROOT`  
+  
+2. All essential dependencies should be installed：pickle, random, re, tqdm, cv2, numpy, pandas, sklearn, keras, tensorflow, keras_contrib, collections.counter.
+   
+# Data set up
+1. Download the NIH Chest X-ray Dataset from here:  
+   https://nihcc.app.box.com/v/ChestXray-NIHCC.  
+   You need to get all the image files (all the files under `images` folder in NIH Dataset), `Data_Entry_2017.csv` file, as well as the      Bounding Box data `BBox_List_2017.csv`.  
+
+2. Create Directory 
+   ```Shell
+   mkdir ROOT/azure-share/chestxray/data/ChestX-ray8/ChestXray-NIHCC
+   mkdir ROOT/azure-share/chestxray/data/ChestX-ray8/ChestXray-NIHCC_other
+   ```  
+3. Save all images under `ROOT/azure-share/chestxray/data/ChestX-ray8/ChestXray-NIHCC`  
+
+4. Save `Data_Entry_2017.csv` and `BBox_List_2017.csv` under `ROOT/azure-share/chestxray/data/ChestX-ray8/ChestXray-NIHCC_other`  
+
+5. Process the Data
+   ```Shell
+   mkdir ROOT/azure-share/chestxray/output/data_partitions
+   ```  
+   Run `000_preprocess.py` to create `*.pickle` files under this directory 
+# Test  
+1. We have provided the pretrained-model `azure_chest_xray_14_weights_712split_epoch_054_val_loss_191.2588.hdf5` under `ROOT/azure-share/chestxray/output/fully_trained_models`. You can also download it separately from [here](https://chestxray.blob.core.windows.net/chestxraytutorial/tutorial_xray/chexray_14_weights_712split_epoch_054_val_loss_191.2588.hdf5).  
+
+2. Run `020_evaluate.py` and it will create `weights_only_azure_chest_xray_14_weights_712split_epoch_054_val_loss_191.2588.hdf5` saving weights of the pretrained-model under the same directory.
+
+3. Below is the result showing the AUC score of all the 14 diseases:  
+
+   | Disease            | Our AUC Score    | Stanford AUC Score | Delta     
+   |--------------------|------------------|--------------------|-----------:
+   | Atelectasis        | 0.822334         | 0.8094             | -0.012934 
+   | Cardiomegaly       | 0.933610         | 0.9248             | -0.008810 
+   | Effusion           | 0.882471         | 0.8638             | -0.018671 
+   | Infiltration       | 0.744504         | 0.7345             | -0.010004 
+   | Mass               | 0.858467         | 0.8676             |  0.009133 
+   | Nodule             | 0.784230         | 0.7802             | -0.004030 
+   | Pneumonia          | 0.800054         | 0.7680             | -0.032054 
+   | Pneumothorax       | 0.829764         | 0.8887             |  0.058936 
+   | Consolidation      | 0.811969         | 0.7901             | -0.021869 
+   | Edema              | 0.894102         | 0.8878             | -0.006302 
+   | Emphysema          | 0.847477         | 0.9371             |  0.089623
+   | Fibrosis           | 0.882602         | 0.8047             | -0.077902 
+   | Pleural Thickening | 1.000000         | 0.8062             | -0.193800 
+   | Hernia             | 0.916610         | 0.9164             | -0.000210   
+   
+# Visualization    
+1. Create Folder Test
+   ```Shell
+   mkdir ROOT/azure-share/chestxray/data/ChestX-ray8/test_images
+   ```  
+   Copy any number of images under `ChestXray-NIHCC` to `test_images` and resize them to 224x224 pixels.  
+
+2. Run `004_cam_simple.py` and it will output a Class Activation Map(CAM). The CAM lets us see which regions in the image were relevant to this class.  
+
+   ![这里随便写文字](https://github.com/fatLime/Predict-Lung-Disease/blob/master/image.png)
+
+# Referenced Paper  
+- Baseline result: https://arxiv.org/abs/1705.02315
+- Image Localization: http://arxiv.org/abs/1512.04150   
+- The original chexnet paper mentioned in [StanfordML website](https://stanfordmlgroup.github.io/projects/chexnet/) as well as their [paper](https://arxiv.org/abs/1711.05225).
+- http://cs231n.stanford.edu/reports/2017/pdfs/527.pdf for pre-processing the data
+- https://arxiv.org/abs/1711.08760 for some other thoughts on the model architecture and the relationship between different diseases
+ 
+# Notes
+  Please contact [email protected] if you have any problem.