SIIM-COVID19-Detection

Source code of the 1st place solution for SIIM-FISABIO-RSNA COVID-19 Detection Challenge.

1.INSTALLATION

• HARDWARE: 4 x NVIDIA TESLA V100 32G GPU, 64 vCPUs, 256GB RAM
• Ubuntu 18.04.5 LTS
• CUDA 10.2
• Python 3.7.9
• python packages are detailed separately in requirements

$ conda create -n envs python=3.7.9
$ conda activate envs
$ conda install -c conda-forge gdcm
$ pip install -r requirements.txt
$ pip install git+https://github.com/ildoonet/pytorch-gradual-warmup-lr.git
$ pip install git+https://github.com/bes-dev/mean_average_precision.git@930df3618c924b694292cc125114bad7c7f3097e

2.DATASET

2.1 SIIM COVID 19 DATASET

• download competition dataset at link then extract to ./dataset/siim-covid19-detection

$ cd src/prepare
$ python dicom2image_siim.py
$ python kfold_split.py
$ python prepare_siim_annotation.py                        # effdet and yolo format
$ cp -r ../../dataset/siim-covid19-detection/images ../../dataset/lung_crop/.
$ python prepare_siim_lung_crop_annotation.py

2.2 EXTERNAL DATASET

• download pneumothorax dataset at link then extract to ./dataset/external_dataset/pneumothorax/dicoms
• download pneumonia dataset at link then extract to ./dataset/external_dataset/rsna-pneumonia-detection-challenge/dicoms
• download vinbigdata dataset at link then extract to ./dataset/external_dataset/vinbigdata/dicoms
• download chest14 dataset at link then extract to ./dataset/external_dataset/chest14/images
• download chexpert high-resolution dataset at link then extract to ./dataset/external_dataset/chexpert/train
• download padchest dataset at link then extract to ./dataset/external_dataset/padchest/images
• _{Note: most of the images in bimcv and ricord duplicate with siim covid trainset and testset. To avoid data-leak when training, I didn't use them. You can use script src/prepare/check_bimcv_ricord_dup.py}

$ cd src/prepare
$ python dicom2image_pneumothorax.py
$ python dicom2image_pneumonia.py
$ python prepare_pneumonia_annotation.py      # effdet and yolo format
$ python dicom2image_vinbigdata.py
$ python prepare_vinbigdata.py
$ python refine_data.py                       # remove unused file in chexpert + chest14 + padchest dataset
$ python resize_padchest_pneumothorax.py

dataset structure should be ./dataset/dataset_structure.txt

3.SOLUTION SUMMARY

4.TRAIN MODEL

4.1 Classification

4.1.1 Multi head classification + segmentation

• Stage1

$ cd src/classification_aux
$ bash train_chexpert_chest14.sh              #Pretrain backbone on chexpert + chest14
$ bash train_rsnapneu.sh                      #Pretrain rsna_pneumonia
$ bash train_siim.sh                          #Train siim covid19

• Stage2: Generate soft-label for classification head and mask for segmentation head.
  Output: soft-label in ./pseudo_csv/[source].csv and public test masks in ./prediction_mask/public_test/masks

$ bash generate_pseudo_label.sh [checkpoints_dir]

• Stage3: Train model on trainset + public testset, load checkpoint from previous round

$ bash train_pseudo.sh [previous_checkpoints_dir] [new_checkpoints_dir]

Rounds of pseudo labeling (stage2) and retraining (stage3) were repeated until the score on public LB didn't improve.

• For final checkpoints

$ bash generate_pseudo_label.sh checkpoints_v3
$ bash train_pseudo.sh checkpoints_v3 checkpoints_v4

• For evaluation

$ CUDA_VISIBLE_DEVICES=0 python evaluate.py --cfg configs/xxx.yaml --num_tta xxx

mAP@0.5 4 classes: negative, typical, indeterminate, atypical

	SeR152-Unet	EB5-Deeplab	EB6-Linknet	EB7-Unet++	Ensemble
w/o TTA/8TTA	0.575/0.584	0.583/0.592	0.580/0.587	0.589/0.595	0.595/0.598

_{8TTA: (orig, center-crop 80%)x(None, hflip, vflip, hflip & vflip). In final submission, I use 4.1.2 lung detector instead of center-crop 80%}

4.1.2 Lung Detector-YoloV5

I annotated the train data(6334 images) using LabelImg and built a lung localizer. I noticed that increasing input image size improves the modeling performance and lung detector helps the model to reduce background noise.

$ cd src/detection_lung_yolov5
$ cd weights && bash download_coco_weights.sh && cd ..
$ bash train.sh

	Fold0	Fold1	Fold2	Fold3	Fold4	Average
mAP@0.5:0.95	0.921	0.931	0.926	0.923	0.922	0.9246
mAP@0.5	0.997	0.998	0.997	0.996	0.998	0.9972

4.2 Opacity Detection

Rounds of pseudo labeling (stage2) and retraining (stage3) were repeated until the score on public LB didn't improve.

4.2.1 YoloV5x6 768

• Stage1:

$ cd src/detection_yolov5
$ cd weights && bash download_coco_weights.sh && cd ..
$ bash train_rsnapneu.sh          #pretrain with rsna_pneumonia
$ bash train_siim.sh              #train with siim covid19 dataset, load rsna_pneumonia checkpoint

• Stage2: Generate pseudo label (boxes)

$ bash generate_pseudo_label.sh

Jump to step 4.2.4 Ensembling + Pseudo labeling

• Stage3:

$ bash warmup_ext_dataset.sh      #train with pseudo labeling (public-test, padchest, pneumothorax, vin) + rsna_pneumonia
$ bash train_final.sh             #train siim covid19 boxes, load warmup checkpoint

4.2.2 EfficientDet D7 768

• Stage1:

$ cd src/detection_efffdet
$ bash train_rsnapneu.sh          #pretrain with rsna_pneumonia
$ bash train_siim.sh              #train with siim covid19 dataset, load rsna_pneumonia checkpoint

• Stage2: Generate pseudo label (boxes)

$ bash generate_pseudo_label.sh

Jump to step 4.2.4 Ensembling + Pseudo labeling

• Stage3:

$ bash warmup_ext_dataset.sh      #train with pseudo labeling (public-test, padchest, pneumothorax, vin) + rsna_pneumonia
$ bash train_final.sh             #train siim covid19, load warmup checkpoint

4.2.3 FasterRCNN FPN 768 & 1024

• Stage1: train backbone of model with chexpert + chest14 -> train model with rsna pneummonia -> train model with siim, load rsna pneumonia checkpoint

$ cd src/detection_fasterrcnn
$ CUDA_VISIBLE_DEVICES=0,1,2,3 python train_chexpert_chest14.py --steps 0 1 --cfg configs/resnet200d.yaml
$ CUDA_VISIBLE_DEVICES=0,1,2,3 python train_chexpert_chest14.py --steps 0 1 --cfg configs/resnet101d.yaml
$ CUDA_VISIBLE_DEVICES=0 python train_rsnapneu.py --cfg configs/resnet200d.yaml
$ CUDA_VISIBLE_DEVICES=0 python train_rsnapneu.py --cfg configs/resnet101d.yaml
$ CUDA_VISIBLE_DEVICES=0 python train_siim.py --cfg configs/resnet200d.yaml --folds 0 1 2 3 4 --SEED 123
$ CUDA_VISIBLE_DEVICES=0 python train_siim.py --cfg configs/resnet101d.yaml --folds 0 1 2 3 4 --SEED 123

_{Note: Change SEED if training script runs into issue related to augmentation (boundingbox area=0) and comment/uncomment the following code if training script runs into issue related to resource limit}

import resource
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
resource.setrlimit(resource.RLIMIT_NOFILE, (8192, rlimit[1]))

• Stage2: Generate pseudo label (boxes)

$ bash generate_pseudo_label.sh

Jump to step 4.2.4 Ensembling + Pseudo labeling

• Stage3:

$ CUDA_VISIBLE_DEVICES=0 python warmup_ext_dataset.py --cfg configs/resnet200d.yaml
$ CUDA_VISIBLE_DEVICES=0 python warmup_ext_dataset.py --cfg configs/resnet101d.yaml
$ CUDA_VISIBLE_DEVICES=0 python train_final.py --cfg configs/resnet200d.yaml
$ CUDA_VISIBLE_DEVICES=0 python train_final.py --cfg configs/resnet101d.yaml

4.2.4 Ensembling + Pseudo labeling

Keep images that meet the conditions: negative prediction < 0.3 and maximum of (typical, indeterminate, atypical) predicion > 0.7. Then choose 2 boxes with the highest confidence as pseudo labels for each image.

_{Note: This step requires at least 128 GB of RAM}

$ cd ./src/detection_make_pseudo
$ python make_pseudo.py
$ python make_annotation.py            

4.2.5 Detection Performance

	YoloV5x6 768	EffdetD7 768	F-RCNN R200 768	F-RCNN R101 1024
mAP@0.5 TTA	0.580	0.594	0.592	0.596

5.FINAL SUBMISSION

siim-covid19-2021 Public LB: 0.658 / Private LB: 0.635
demo notebook to visualize output of models

6.AWESOME RESOURCES

Pytorch✨
PyTorch Image Models✨
Segmentation models✨
EfficientDet✨
YoloV5✨
FasterRCNN FPN✨
Albumentations✨
Weighted boxes fusion✨