RSNA Intracranial Hemorrhage Detection

Experiments

exp window input output comment
exp7_seres_precenter_512 subdural st st for external data pseudo labeling
exp10_seres_threecenter_rescale_512 3types st st
exp16_seres_concat subdural st-1, st, st+1 st
exp17_7_external subdural st st with external data
exp18_seres_concat_three 3types st-1, st, st+1 st
exp19_seres_doubleconcat subdural st-2, st, st+2 st
exp21_seres_doublepre subdural st-2, st-1, st st
exp22_seres_doublepost subdural st, st+1, st+2 st
exp23_seres_doublepre_three 3types st-2, st-1, st st
exp24_seres_doublepost_three 3types st, st+1, st+2 st
exp25_seres_conc3 subdural mean(st-3, st-2, st-1), st, mean(st+1, st+2, st+3) st
exp26_seres_concall_prepost subdural mean(all st's), st, mean(st-1, st+1) st
exp27_seres_conc5 subdural mean(st-5, st-4, st-3, st-2, st-1), st, mean(st+1, st+2, st+3, st+4, st+5) st
exp28_seres101_16 subdural st-1, st, st+1 st seresnext101
exp32_seres_conc_any subdural st-1, st, st+1 st predict 5 classes and fill_“any” on max prediction.
exp34_seres_threetarget subdural st-1, st, st+1 st-1, st, st+1
exp36_seres_double_threetarget subdural st-2, st, st+2 st-2, st, st+2

Usage

  1. Download datasets in input dir.

  2. preprocessing

sh bin/preprocess.sh
  1. train
sh bin/train.sh
  1. predict
sh bin/predict.sh
  1. stacking sry this part of code is super dirty...

run kernel.
then download output file in output dir.

sh bin/stack.sh

Usage Demo

only predition with pretrained models. This pipeline use user stacking only. The private score=0.04393.

1.Download models from kaggle datasets.

2.Run prediction

sh bin/demo.sh

Hardware and environment

GCP deep learning vm

  • gpu: V100*2
  • cpu: n1-standard-16(16 vCPU, 60GM RAM)

Install

pip install -r requirements.txt

License

The license is MIT.