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 :

Package :

Pytorch==0.2.0   torchvision==0.2.0   matplotlib   scikit-image==0.13.1   opencv_python==3.4.0.12   numpy==1.13.3  matplotlib==2.1.1  scipy==1.0.0   sklearn==0.19.1  

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. DeepQ testing:

upload deepQ_25.zip to the platform. Then use following command:

python3 inference.py

Note :

In our .py script, I used the following script to assign the task running on GPU 0.

import os
os.environ['CUDA_VISIBLE_DEVICES'] = "0"

Result :

Prediction

Heatmap per disease Bounding Box per patient