Weakly Supervised Learning for Findings Detection in Medical Images
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)
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:
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)]
python3 train.py [path of preprocessed output (folder)]
python3 denseNet_localization.py [path to test.txt] [path of images folder]
[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
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"
Prediction
Heatmap per disease
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 Visualization of some images with its ground-truth label (red) and its prediction (blue) selected from each disease class.
Refers to the report for more experiment results.
Feel free to contact me ([email protected]) if you have any problem.