SIIM-ACR Pneumothorax Segmentation Competition In Kaggle

Code for kaggle siim-acr-pneumothorax-segmentation, 34th place solution.

The details of the solution can be found here

Requirements

• Pytorch 1.1.0
• Torchvision 0.3.0
• Python3.7
• scipy 1.2.0
• Install backboned-unet first

pip install git+https://github.com/mkisantal/backboned-unet.git

• Install image augumentation library albumentations

conda install -c conda-forge imgaug
conda install albumentations -c albumentations

• Install TensorBoard for Pytorch

pip install tb-nightly
pip install future

• Install segmentation_models.pytorch

pip install git+https://github.com/qubvel/segmentation_models.pytorch

If you encountered error like: ValueError: Duplicate plugins for name projector when you are evacuating tensorboard --logdir=checkpoints/unet_resnet34, please refer to: this.

I downloaded a test script from https://raw.githubusercontent.com/tensorflow/tensorboard/master/tensorboard/tools/diagnose_tensorboard.py
I run it and it told me that I have two tensorboards with a different version. Also, it told me how to fix it.
I followed its instructions and I can make my tensorboard work.

I think this error means that you have two tensorboards installed so the plugin will be duplicated. Another method would be helpful that is to reinstall the python environment using conda.

Segmentation Results

The followings are some visualizations of our results.

Using only one model:

There are three parts in the image above. The left part is patient's X-ray picture, the middle is the original mask, the right is our segmentation mask.

Using five models ensemble:

How to run

Clone Our Project

git clone https://github.com/XiangqianMa/Kaggle-Pneumothorax-Seg.git
cd Kaggle-Pneumothorax-Seg

Prepare Dataset

Download SIIM datasets from here , unzip and put them into ../input directory. Structure of the ../input folder can be like:

dicom-images-test
dicom-images-train
stage_2_images
stage_2_train.csv
train-rle.csv

Delete some non-annotated instances/images:

cd dicom-images-test
rm */*/1.2.276.0.7230010.3.1.4.8323329.6491.1517875198.577052.dcm
rm */*/1.2.276.0.7230010.3.1.4.8323329.7013.1517875202.343274.dcm
rm */*/1.2.276.0.7230010.3.1.4.8323329.6370.1517875197.841736.dcm
rm */*/1.2.276.0.7230010.3.1.4.8323329.6082.1517875196.407031.dcm
rm */*/1.2.276.0.7230010.3.1.4.8323329.7020.1517875202.386064.dcm

Put stage_2_sample_submission.csv into Kaggle-Pneumothorax-Seg directory. Then，evacuate the following instructions to convert original dcm files to jpg.

cd Kaggle-Pneumothorax-Seg 
python datasets/dcm2jpg.py
cd ../input/

mkdir train_images_all
cp train_images/* train_images_all/
cp test_images/* train_images_all/

mkdir train_mask_all
cp train_mask/* train_mask_all/
cp test_mask/* train_mask_all/

Create soft links of datasets in the following directories:

cd ../Kaggle-Pneumothorax-Seg/datasets/
mkdir SIIM_data
cd SIIM_data
ln -s ../../../input/train_images/ train_images
ln -s ../../../input/train_mask/ train_mask
ln -s ../../../input/test_images/ test_images

ln -s ../../../input/test_mask test_mask
ln -s ../../../input/test_images_stage2 test_images_stage2
ln -s ../../../input/train_images_all/ train_images_all
ln -s ../../../input/train_mask_all/ train_mask_all

Data Analysis

Before our training, we can use datasets_statics.py to analyze the distribution of training data:

python utils/datasets_statics.py

You can get something like:

Train

Use one gpu for Stratified K-fold:

CUDA_VISIBLE_DEVICES=0 python train_sfold_stage2.py

Use all gpu for Stratified K-fold:

python train_sfold_stage2.py

The competition is divided into two stages, so if you want to run the code for the first stage, please run python train_sfold.py

Please note that, if you prepare to use deeplabv3+ model, please add drop_last=True to all DataLoader functions in datasets/siim.py.

Tensorboard

After the training of model, we can use tensorboard to analyze the training curves.

Different Event Files

Tensorboard displays different event files:

tensorboard --logdir=name1:/path/to/logs/1,name2:/path/to/logs/2

For example, when the files in the checkpoints/unet_resnet34 folder are organized as following:

├── 2019-08-27T22-59-29
│   └── events.out.tfevents.1564306811.zdkit.25995.0
├── 2019-08-28T02-01-21
│   └── events.out.tfevents.1564324685.zdkit.25995.1

We can run:

cd checkpoints/unet_resnet34
tensorboard --logdir=name1:2019-08-27T22-59-29,name2:2019-08-28T02-01-21

One Event File

Tensorboard displays one event file:

tensorboard --logdir=/path/to/logs

For example, when the files in the checkpoints/unet_resnet34 folder are as follows

├── 2019-08-27T22-59-29
│   └── events.out.tfevents.1564306811.zdkit.25995.0

You can run:

cd checkpoints/unet_resnet34
tensorboard --logdir=2019-08-27T22-59-29

Choose Threshold

python train_sfold_stage2.py --mode=choose_threshold2
python train_sfold_stage2.py --mode=choose_threshold3

After running this，the best threshold and the best pixel threshold will be saved in the checkpoints/unet_resnet34 folder

Create Prediction Csv

python create_submission.py

After running the code, submission.csv will be generated in the root directory, which is the result predicted by the model.

Demo

When you have trained and selected the threshold, you can use demo_on_val.py to visualize the performance on the validation set

python demo_on_val.py

It is important to note that this code is only suitable for testing the performance of the fold0, for complete cross-validation, there is no handout datasets, so using this code can not measure the generalization ability of the model.

Others

At the end of the first stage of the competition, the competitor released the test dataset labels for the first stage. So we wrote a code to measure the performance of our first stage model (using dice)

python test_on_stage1.py

Results

Old Submission.csv

backbone	batch_size	image_size	pretrained	data proprecess	mask resize	less than sum	T	lr	thresh	sum	score
U-Net	32	224	w/o	w/o	w/o	w/o	w/o	random			0.7019
ResNet34	32	224	w/	w/o	w/o	w/o	w/o	random			0.7172
ResNet34	32	224	w/o	w/o	w/o	w/o	w/o	random			0.7295
ResNet34	20	512	w/	w/o	w/o	w/o	w/o	random			0.7508
ResNet34	20	512	w/	w/	w/o	w/o	w/o	random			0.7603
ResNet34	20	512	w/	w/	w	w/o	w	random			0.7974
ResNet34	20	512	w/	w/	w	1024*2	w/o	random			0.7834
ResNet34	20	512	w/	w/	w	2048*2	w	random		115	0.8112
ResNet34 freeze	20	512	w/	w/	w	2048*2	w	random		107	0.8118
ResNet34 freeze	20	512	w/	w/	w/	2048*2	w	CosineAnnealingLR	0.45	164	0.8259
ResNet34 freeze	20	512	w/	w/ CLAHE	w/	2048*2	w	CosineAnnealingLR	0.47	208	0.8401
ResNet34 freeze	20	512	w/	w/ CLAHE	w/	2048*2	w	CosineAnnealingLR	0.40	225	0.8412
ResNet34 freeze	20	512	w/	w/ CLAHE	w/	2048*2	w	CosineAnnealingLR	0.36	-	0.8446
ResNet34 freeze/No accumulation	20/8	512/1024	w/	w/ CLAHE	512	2048*2	w	CosineAnnealingLR	0.48	210	0.8419
ResNet34 freeze/No accumulation	20/8	512/1024	w/	w/ CLAHE	1024	1024*2	w	CosineAnnealingLR	0.48	118	0.7969
ResNet34 freeze/No accumulation	20/8	512/1024	w/	w/ CLAHE	1024	1024*2	w	CosineAnnealingLR	0.30	172	0.7958
ResNet34 freeze/No accumulation	8	1024	w/	w/ CLAHE	1024	2048*2	w	CosineAnnealingLR	0.35	209	0.8399
ResNet34/No accumulation	20	768	w/	w/ CLAHE	1024	2048	w	CosineAnnealingLR	0.46	249(ensemble)	0.8455

New Submission.csv

backbone	batch_size	image_size	pretrained	data proprecess	lr	loss function	thresh	less than sum	ensemble	sum	score
ResNet34/No accumulation	20	768	w/	w/ CLAHE	CosineAnnealingLR		0.46	2048	average	171	0.8588
ResNet34/No accumulation	20	1024	w/	w/ CLAHE	CosineAnnealingLR	BCE	0.306	2048	average	207	0.8648
ResNet34/No accumulation	20	1024	w/	w/ CLAHE	CosineAnnealingLR	BCE	0.328	1024	average	223	0.8619
ResNet34/No accumulation	20	1024	w/	w/ CLAHE	CosineAnnealingLR	bce	0.34	2048	None	224	0.8535
ResNet34(New)/No accumulation	20	768	w/	w/ CLAHE	CosineAnnealingLR	bce	0.5499	2048	None	172	0.8503
ResNet34(New)/No accumulation	20	1024	w/	w/ CLAHE	CosineAnnealingLR	bce	0.3800	1792	None	228	0.8505
ResNet34(New)/No accumulation	20	1024	w/	w/ CLAHE	CosineAnnealingLR	bce+dice+weight	0.72	1024	None	195	0.8539
ResNet34(New)/No accumulation	10/6	1024	w/	w/ 0.4CLAHE	CosineAnnealingLR(2e-4/5e-6)	bce+dice+weight	0.67	2048	TTA/None	207	0.8691
ResNet34(New)/No accumulation	10/6	1024	w/	w/ 0.4CLAHE	CosineAnnealingLR(2e-4/1e-5)	bce+dice+weight	0.75	1280	TTA/None	219	0.8571
ResNet34(New)/No accumulation	10/6	1024	w/	w/ 0.4CLAHE self	CosineAnnealingLR(2e-4/5e-6)	bce+dice+weight	0.75	1024	TTA/None	217	0.8575
ResNet34(new)/No accumulation	10/6	1024	w/	w/ 0.4CLAHE	CosineAnnealingLR(2e-4/5e-6)	bce	0.45	1024	TTA/None	236	0.8570
ResNet34/No accumulation	10/6	1024	w/	w/ 0.4CLAHE	CosineAnnealingLR(2e-4/5e-6)	bce	0.36	768	TTA/None	254	0.8555
ResNet34(New)/No accumulation/three stage	10/6/6	1024	w/	w/ 0.4CLAHE	CosineAnnealingLR(2e-4/5e-6/1e-7)	bce+dice+weight	0.67	2048	TTA/None	206	0.8741

MileStone

• 0.8446: fixed test code, used resize(1024)
• 0.8648: used more large resolution (516->768), and average ensemble (little)
• 0.8691: bce+dice+weight (matters a lot/1.21); TTA (matters little); In the first stage, the epoch was reduced from 60 to 40, and the learning rate was reduced to 0 at the 50th epoch. The second stage of learning is adjusted to 5e-6 (matters a lot); Change the data preprocessing mode, the CLAHE probability is changed to 0.4, the vertical flip is removed, the rotation angle is reduced, and the center cutting is added.
• 0.8741: three stage set: Load the weights of the second phase and train only on masked datasets.

Tricks Tried

• unet_resnet34(matters a lot)
• two stage set: two stage batch size(768,1024 big solution matters a lot) and two stages epoch
• epoch freezes the encoder layer in the first stage
• epoch gradients accumulate in the second stage
• data augmentation
• CLAHE for every picture(matters a little)
• lr decay - cos annealing(matters a lot)
• cross validation
• Stratified K-fold
• Average each result of cross validation(matters a lot)
• stage2 init lr and optimizer
• weight decay(When equal to 5e-4, the negative effect, val loss decreases and dice oscillates, the highest is 0.77)
• leak, TTA
• datasets_statics and choose less than sum
• adapt to torchvison0.2.0, tensorboard
• different Learning rates between encoder and decoder in stage2 (not well)
• freeze BN in stage2 (not well)
• using lovasz loss in stage2 (this loss can be used to finetune model) (not well)
• replace upsample (interplotation) with transpose convolution (not well)
• using octave convolution in unet's decoder (not well)
• resnet34->resnet50 (a wider model can work better with bigger resolution) (not well)
• move noise form augmentation (not well)
• Unet with Attention (not test, the model is too big, so that the batch size is too small)
• change from 5 flod to 10 fold (not well)
• hypercolumn unet (not well)
• Dataset expansion (not well)
• Data expansion is used only in the 1/3/10 epoch in the first stage (not well)
• deeplabv3+ (not work)
• Recitified Adam(Radams) (not work)
• three stage set: Load the weights of the second phase and train only on masked datasets(matters a lot，from 0.8691 to 0.8741)
• the dice coefficient is unstale in val set (The code is wrong.WTF)