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The 4th Place Solution of SIIM-ACR Pneumothorax Segmentation

Solution

My solution is based on UNet with a deep supervision branch for empty mask classification.

Model

  • Model: UNet.
  • Backbone: ResNet34 backbone with frozen batch-normalization.
  • Preprocessing: training on random crops with (512, 512) size, inference on (768, 768) size.
  • Augmentations: ShiftScaleRotate, RandomBrightnessContrast, ElasticTransform, HorizontalFlip from albumentations.
  • Optimizer: Adam, batch_size=8
  • Scheduler: CosineAnnealingLR
  • Additional feature: the proportion of non-empty samples linearly decreased from 0.8 to 0.22 (as in train dataset) depending on the epoch. It helped to converge faster.
  • Loss: 2.7 * BCE(pred_mask, gt_mask) + 0.9 * DICE(pred_mask, gt_mask) + 0.1 * BCE(pred_empty, gt_empty). Here pred_mask is the prediction of the UNet, pred_empty is the prediction of the branch for empty mask classification.
  • Postprocessing: if pred_empty > 0.4 or area(pred_mask) < 800: pred_mask = empty. Parameters are selected on the validation set.
  • Ensemble: averaging the 4 best checkpoints over 8 folds, horizontal flip TTA.
  • Hardware: 4 x RTX 2080

Docker

make build
make run
make exec

How to run?

cd scripts
bash train.sh
bash test.sh
bash ensemble.sh
bash submit.sh

References