Kaggle_RSNA2020_6th_Solution

This is repository of the 6th place solution of kaggle RSNA STR Pulmonary Embolism Detection.
The discription of this solution is available here.
The prediction notebook in the competition is available here.

Enviroment

Google Cloud Platform

• SMP Debian 4.9.210-1 (2020-01-20)
• n1-standard-16 (vCPU x 16, memory 60 GB)
• 1 x NVIDIA Tesla P100

Requirements

• Python 3.7.6
• CUDA 10.1
• cuddn 7.6.3
• gdcm 2.8.9
• numpy==1.19.1
• pandas==1.1.1
• matplotlib==3.2.1
• opencv-python==4.3.0.36
• pydicom==2.0.0
• scikit-learn==0.23.1
• torch==1.6.0+cu101
• torchvision==0.7.0+cu101
• timm==0.1.26
• albumentations==0.4.5

Data setup

Download the competition dataset and place them in input/orig/.
In case you use pretrained weights, download the weights and place them to models/.

Training and Prediction

Run all the cells of notebook/preprocess.ipynb.
Run all the cells of notebook/train_stage1.ipynb.
run all cells of notebook/train_stage2.ipynb.
Rewrite the line 9 of the 5th cell of notebook/train_stage1.ipynb to MODEL_NAME = 'b2' and run all the cells.
Rewrite the line 8 of the 5th cell of notebook/train_stage1.ipynb to MODEL_NAME = 'b2' and run all the cells.
Run all the cells of notebook/postprocess.ipynb.
Run all the cells of notebook/predict.ipynb.

Prediction with the pretrained weights

Rewrite the line 12-15 of the 3rd cell of notebook/predict.ipynb to

weight_dir_b0_1 = "../models/b0_stage1"
weight_dir_b0_2 = "../models/b0_stage2"
weight_dir_b2_1 = "../models/b2_stage1"
weight_dir_b2_2 = "../models/b2_stage2"

and run all the cells.