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Digital Medicine 2020 Fall: RSNA ICH Detection

[slide]

Task

Design and present an analysis flow for RSNA ICH classifiers.

Environment

  • Ubuntu 20.04.1 LTS
  • Python 3.7.5+
  • GeForce GTX 1080 Ti
  • CUDA version 11.0+

Packages

  • tqdm
  • numpy
  • pandas
  • pydicom
  • seaborn
  • openpyxl
  • matplotlib
  • scikit-learn 0.23.2+
  • pytorch 1.7.0+
  • torchvision 0.8.0+

Prerequsites

pip3 install -r requirements.txt

Usage

Training example

python3 train.py \
--exp_name='' \
--model_name googlenet \
--generate_exp_name --name_split_file --pretrained --radam \
--random_apply_aug --random_horizontal_flip --random_rotation --random_erasing --random_order \
--cutmix

Testing example

python3 test.py \
--model_name googlenet \
--exp_name='_Pretrain_googlenet_E100_lr0.0005_b1_0.9_b2_0.999_bs_16_splt_0.7_prekeral_bsb_radam_cutmixbeta_1_cutmixprob_0.5_rand_app_rand_flip_rand_rota_rand_ord_rand_eras' \
--cpt_name='googlenet_E_79_iter_21040.cpt'

Supported models

  • resnet18, resnet34, resnet50, resnet101, resnet152
  • resnext50_32x4d, resnext101_32x8d
  • wide_resnet50_2, wide_resnet101_2
  • densenet121, densenet169, densenet161, densenet201
  • googlenet

Our method

Data preprocessing

Data Augmentation

  • Random Apply
  • Random Order
  • Random Horizontal Flip
  • Random Rotation
  • Random Erasing / Cutout
    • Zhong, Zhun, et al. "Random Erasing Data Augmentation." AAAI. 2020.
    • DeVries, Terrance et al. "Improved regularization of convolutional neural networks with cutout." arXiv preprint 2017.
  • Mixup
    • Zhang, Hongyi, et al. "mixup: Beyond Empirical Risk Minimization." ICLR 2018
  • Cutmix
    • Yun, Sangdoo, et al. "Cutmix: Regularization strategy to train strong classifiers with localizable features." ICCV. 2019.

Model

Fine-tune models in torchvision.models with ImageNet pretrained weight

Experiment results

https://docs.google.com/spreadsheets/d/1HslC0VL4noqPN9LpZZlKMbwCwXAd38pN6IiANSGjKDk/edit?usp=sharing

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