This is the source code for the first place solution to the RSNA2019 Intracranial Hemorrhage Detection Challenge.
Solution write up: Link.
- opencv-python==3.4.2
- scikit-image==0.14.0
- scikit-learn==0.19.1
- scipy==1.1.0
- torch==1.1.0
- torchvision==0.2.1
- 2DNet
- 3DNet
- SequenceModel
- seresnext101_256*256 [seresnext101]
- densenet169_256*256 [densenet169]
- densenet121_512*512 [densenet121]
Prepare csv file:
download data.zip: https://drive.google.com/open?id=1buISR_b3HQDU4KeNc_DmvKTYJ1gvj5-3
- convert dcm to png
python3 prepare_data.py -dcm_path stage_1_train_images -png_path train_png
python3 prepare_data.py -dcm_path stage_1_test_images -png_path train_png
python3 prepare_data.py -dcm_path stage_2_test_images -png_path test_png
- train
python3 train_model.py -backbone DenseNet121_change_avg -img_size 256 -tbs 256 -vbs 128 -save_path DenseNet121_change_avg_256
python3 train_model.py -backbone DenseNet169_change_avg -img_size 256 -tbs 256 -vbs 128 -save_path DenseNet169_change_avg_256
python3 train_model.py -backbone se_resnext101_32x4d -img_size 256 -tbs 80 -vbs 40 -save_path se_resnext101_32x4d_256
- predict
python3 predict.py -backbone DenseNet121_change_avg -img_size 256 -tbs 4 -vbs 4 -spth DenseNet121_change_avg_256
python3 predict.py -backbone DenseNet169_change_avg -img_size 256 -tbs 4 -vbs 4 -spth DenseNet169_change_avg_256
python3 predict.py -backbone se_resnext101_32x4d -img_size 256 -tbs 4 -vbs 4 -spth se_resnext101_32x4d_256
After single models training, the oof files will be saved in ./SingleModelOutput(three folders for three pipelines).
After training the sequence model, the final submission will be ./FinalSubmission/final_version/submission_tta.csv
Set data path in ./setting.py
download [csv.zip]
download [feature samples]
CUDA_VISIBLE_DEVICES=0 python main.py
The final submissions are in the folder ../FinalSubmission/version2/submission_tta.csv
- 0.04383
- Pre-trained models
- 2DCNN + SeqModel end-to-end training
- 3DCNN training
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