Body morphometry for sarcopenia (Kaggle Competition)

MOAI 2020 Body Morphometry AI Segmentation Online Challenge

[Competition Page]

Private Leaderboard : 2nd (Hallym MMC)

[[Solution]](./kaggle solution 설명.pdf)

body-morp-segmentation

Please see the attached pdf file named '파이프라인 설명서.pdf'

Model Zoo

• AlbuNet (resnet34) from [ternausnets]
• Resnet50 from [selim_sef SpaceNet 4]
• SCSEUnet (seresnext50) from [selim_sef SpaceNet 4]

Original source code

https://github.com/sneddy/pneumothorax-segmentation

Video with short explanation: https://youtu.be/Wuf0wE3Mrxg

Presentation with short explanation: https://yadi.sk/i/oDYnpvMhqi8a7w

Competition: https://kaggle.com/c/siim-acr-pneumothorax-segmentation

Main Features

Combo loss

Used [combo loss] combinations of BCE, dice and focal. In the best experiments the weights of (BCE, dice, focal), that I used were:

• (3,1,4) for albunet_valid and seunet;
• (1,1,1) for albunet_public;
• (2,1,2) for resnet50.

Why exactly these weights?

In the beginning, I trained using only 1-1-1 scheme and this way I get my best public score.

I noticed that in older epochs, Dice loss is higher than the rest about 10 times.

For balancing them I decide to use a 3-1-4 scheme and it got me the best validation score.

As a compromise I chose 2-1-2 scheme for resnet50)

Checkpoints averaging

Top3 checkpoints averaging from each fold from each pipeline on inference

Horizontal flip TTA

File structure

├── unet_pipeline
│   ├── experiments
│   │   ├── some_experiment
│   │   │   ├── train_config.yaml
│   │   │   ├── inference_config.yaml
│   │   │   ├── submit_config.yaml
│   │   │   ├── checkpoints
│   │   │   │   ├── fold_i
│   │   │   │   │   ├──topk_checkpoint_from_fold_i_epoch_k.pth 
│   │   │   │   │   ├──summary.csv
│   │   │   │   ├──best_checkpoint_from_fold_i.pth
│   │   │   ├── log
├── input                
│   ├── dicom_train
│   │   ├── some_folder
│   │   │   ├── some_folder
│   │   │   │   ├── some_train_file.dcm
│   ├── dicom_test   
│   │   ├── some_folder
│   │   │   ├── some_folder
│   │   │   │   ├── some_test_file.dcm
|   ├── new_sample_submission.csv
│   └── new_train_rle.csv
└── requirements.txt

Install

pip install -r requirements.txt

Data Preparation

kaggle competitions download -c body-~~~

Pipeline launch example

Training:

cd unet_pipeline
python Train.py experiments/albunet512/train_config_part0.yaml
python Train.py experiments/albunet512/train_config_part1.yaml

As an output, we get a checkpoints in corresponding folder.

Inference:

cd unet_pipeline
python Inference.py experiments/albunet512/inference_config.yaml

As an output, we get a pickle-file with mapping the file name into a mask with pneumothorax probabilities.

Submit:

cd unet_pipeline
python TripletSubmit.py experiments/albunet512/submit.yaml

As an output, we get submission file with rle.