RSNA Intracranial Hemorrhage Detection with python

Last update: Nov 30, 2022

Overview

RSNA Intracranial Hemorrhage Detection

This is the source code for the first place solution to the RSNA2019 Intracranial Hemorrhage Detection Challenge.

Solution write up: Link.

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]
densenet169_256*256 [densenet169]
densenet121_512*512 [densenet121]

Preprocessing

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

Sequence Models

Sequence Model 1

Sequence Model 2

Path Setup

Set data path in ./setting.py

download

download [csv.zip]

download [feature samples]

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:

  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

RSNA Intracranial Hemorrhage Detection with python

Related tags

Overview

RSNA Intracranial Hemorrhage Detection

Solutuoin Overview

Dependencies

CODE

2D CNN Classifier

Pretrained models

Preprocessing

Sequence Models

Sequence Model 1

Sequence Model 2

Path Setup

download

Sequence Model Training

Final Submission

Private Leaderboard:

Reference

TODO

Owner

Code for Active Learning at The ImageNet Scale.

A Fast and Accurate One-Stage Approach to Visual Grounding, ICCV 2019 (Oral)

[ICCV 2021] Official Pytorch implementation for Discriminative Region-based Multi-Label Zero-Shot Learning SOTA results on NUS-WIDE and OpenImages

This is the repo for the paper "Improving the Accuracy-Memory Trade-Off of Random Forests Via Leaf-Refinement".

Unofficial PyTorch implementation of Neural Additive Models (NAM) by Agarwal, et al.

Code release for Local Light Field Fusion at SIGGRAPH 2019

STRIVE: Scene Text Replacement In Videos

InvTorch: memory-efficient models with invertible functions

mbrl-lib is a toolbox for facilitating development of Model-Based Reinforcement Learning algorithms.

This repository is for Contrastive Embedding Distribution Refinement and Entropy-Aware Attention Network (CEDR)

An implementation of IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification

CCP dataset from Clothing Co-Parsing by Joint Image Segmentation and Labeling

Official code release for 3DV 2021 paper Human Performance Capture from Monocular Video in the Wild.

MoveNetを用いたPythonでの姿勢推定のデモ

Unofficial implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" (https://arxiv.org/abs/2103.14030)

Official implementation for "Style Transformer for Image Inversion and Editing" (CVPR 2022)

Some pvbatch (paraview) scripts for postprocessing OpenFOAM data

magiCARP: Contrastive Authoring+Reviewing Pretraining

KAPAO is an efficient multi-person human pose estimation model that detects keypoints and poses as objects and fuses the detections to predict human poses.

Generate indoor scenes with Transformers