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--- a/README.md +++ b/README.md @@ -1,110 +1,110 @@ -# A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans - -[Journal Link](https://doi.org/10.1016/j.nicl.2021.102785) - -# RSNA Intracranial Hemorrhage Detection -This is the source code for the first place solution to the [RSNA2019 Intracranial Hemorrhage Detection Challenge](https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection). - -Solution write up: [Link](https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection/discussion/117210#latest-682640). - -## Solutuoin Overview - - -#### Dependencies -- 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 - -### CODE -- 2DNet -- 3DNet -- SequenceModel - -# 2D CNN Classifier - -## Pretrained models -- seresnext101_256*256 [\[seresnext101\]](https://drive.google.com/open?id=18Py5eW1E4hSbTT6658IAjQjJGS28grdx) -- densenet169_256*256 [\[densenet169\]](https://drive.google.com/open?id=1vCsX12pMZxBmuGGNVnjFFiZ-5u5vD-h6) -- densenet121_512*512 [\[densenet121\]](https://drive.google.com/open?id=1o0ok-6I2hY1ygSWdZOKmSD84FsEpgDaa) - -## Preprocessing - - -Prepare csv file: - -download data.zip: https://drive.google.com/open?id=1buISR_b3HQDU4KeNc_DmvKTYJ1gvj5-3 - -1. 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 -``` - -2. 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 -``` - -3. 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 - -# Sequence Models - -## Sequence Model 1 - - -## Sequence Model 2 - - -#### Path Setup -Set data path in ./setting.py - -#### download - -download [\[csv.zip\]](https://drive.google.com/open?id=1qYi4k-DuOLJmyZ7uYYrnomU2U7MrYRBV) - -download [\[feature samples\]](https://drive.google.com/open?id=1lJgzZoHFu6HI4JBktkGY3qMk--28IUkC) - -#### Sequence Model Training -``` -CUDA_VISIBLE_DEVICES=0 python main.py -``` -The final submissions are in the folder ../FinalSubmission/version2/submission_tta.csv - -## Final Submission -### Private Leaderboard: -- 0.04383 -## Reference -If you find our work useful in your research or if you use parts of this code please consider citing our [paper](https://doi.org/10.1016/j.nicl.2021.102785): - -```@article{wang2021deep, - title={A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans}, - author={Wang, Xiyue and Shen, Tao and Yang, Sen and Lan, Jun and Xu, Yanming and Wang, Minghui and Zhang, Jing and Han, Xiao}, - journal={NeuroImage: Clinical}, - volume={32}, - pages={102785}, - year={2021}, - publisher={Elsevier} -} -``` - - -### TODO -- [ ] Pre-trained models -- [ ] 2DCNN + SeqModel end-to-end training -- [ ] 3DCNN training +# A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans + +[Journal Link](https://doi.org/10.1016/j.nicl.2021.102785) + +# RSNA Intracranial Hemorrhage Detection +This is the source code for the first place solution to the [RSNA2019 Intracranial Hemorrhage Detection Challenge](https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection). + +Solution write up: [Link](https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection/discussion/117210#latest-682640). + +## Solutuoin Overview + + +#### Dependencies +- 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 + +### CODE +- 2DNet +- 3DNet +- SequenceModel + +# 2D CNN Classifier + +## Pretrained models +- seresnext101_256*256 [\[seresnext101\]](https://drive.google.com/open?id=18Py5eW1E4hSbTT6658IAjQjJGS28grdx) +- densenet169_256*256 [\[densenet169\]](https://drive.google.com/open?id=1vCsX12pMZxBmuGGNVnjFFiZ-5u5vD-h6) +- densenet121_512*512 [\[densenet121\]](https://drive.google.com/open?id=1o0ok-6I2hY1ygSWdZOKmSD84FsEpgDaa) + +## Preprocessing + + +Prepare csv file: + +download data.zip: https://drive.google.com/open?id=1buISR_b3HQDU4KeNc_DmvKTYJ1gvj5-3 + +1. 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 +``` + +2. 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 +``` + +3. 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 + +# Sequence Models + +## Sequence Model 1 + + +## Sequence Model 2 + + +#### Path Setup +Set data path in ./setting.py + +#### download + +download [\[csv.zip\]](https://drive.google.com/open?id=1qYi4k-DuOLJmyZ7uYYrnomU2U7MrYRBV) + +download [\[feature samples\]](https://drive.google.com/open?id=1lJgzZoHFu6HI4JBktkGY3qMk--28IUkC) + +#### Sequence Model Training +``` +CUDA_VISIBLE_DEVICES=0 python main.py +``` +The final submissions are in the folder ../FinalSubmission/version2/submission_tta.csv + +## Final Submission +### Private Leaderboard: +- 0.04383 +## Reference +If you find our work useful in your research or if you use parts of this code please consider citing our [paper](https://doi.org/10.1016/j.nicl.2021.102785): + +```@article{wang2021deep, + title={A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans}, + author={Wang, Xiyue and Shen, Tao and Yang, Sen and Lan, Jun and Xu, Yanming and Wang, Minghui and Zhang, Jing and Han, Xiao}, + journal={NeuroImage: Clinical}, + volume={32}, + pages={102785}, + year={2021}, + publisher={Elsevier} +} +``` + + +### TODO +- [ ] Pre-trained models +- [ ] 2DCNN + SeqModel end-to-end training +- [ ] 3DCNN training