RSNA Intracranial Hemorrhage Detection
This is the project for RSNA Intracranial Hemorrhage Detection hosted on Kaggle in 2019.
Performance (Single model)
|se_resnext50_32x4d||512x512||0.070 - 0.072|
For this challenge, windowing is important to focus on the matter, in this case the brain and the blood. There are good kernels explaining how windowing works.
We used three types of windows to focus and assigned them to each of the chennel to construct images on the fly for training.
|Channel||Matter||Window Center||Window Width|
./input directory in the root level and unzip the file downloaded from kaggle there. All other directories such as
./model will be created if needed when
./bin/preprocess.sh is run.
Please make sure you run the script from parent directory of
$ sh ./bin/preprocess.sh
preprocess.sh does the following at once.
- dicom_to_dataframe.py reads dicom files and save its metadata into the dataframe.
- create_dataset.py creates a dataset for training.
- make_folds.py makes folds for cross validation.
$ sh ./bin/train001.sh
$ sh ./bin/predict001.sh
predict001.sh does the predictions and makes a submission file for scoring on Kaggle. Please uncomment the last line if you want to automatically submit it to kaggle through API.