HuBMAP 17th place model pipeline

Kaggle competition: HuBMAP - Hacking the Kidney
Identify glomeruli in human kidney tissue images

competition overview: https://www.kaggle.com/c/hubmap-kidney-segmentation/overview

solution detail: https://www.kaggle.com/c/hubmap-kidney-segmentation/discussion/238027

Transcript

1. Preprocessing / Augmentation
   • Naive normalization; divided by 255
   • 5 folds; 3 WSI for each fold
   • Augmentations (p=0.5)
   - Horizontal/Vertical Flip (p=0.5)
   - RandomRotate90 (p=0.5)
   - Rotate (-40/+40, reflect101, p=0.5)
   - ShiftScaleRotate (scale: -0.2/0.1, p=0.5)
   - OneOf (p=0.5)
   RandomBrightnessContrast (B: 0.5, C: 0.1)
   HueSaturationValue (H: +-20, S: +-100, V: +-80)
   - One of (p=0.5)
   Cutout (holes: 100, size: 1/64, white RGB)
   GaussianNoise
   - One of (p=0.5)
   ElasticTransform
   GridDistortion (steps: 5, limit: 0.3)
   OpticalDistortion (distort: 0.5, shift: 0.0)
   Cutting out patches
   • Remove black/white pathes for training
   - White (R:217, G:213, B:217) color information
   was used to add noise and fill the edges of the
   images in augmentation.
   - Not removing black/white pathes for validation
   images
   • Different scale patches for ensemble
   Group A (for final submission):
   Size = 352, Down scale = 4
   Size = 512, Down scale = 2
   Group B:
   Size = 352, Down scale = 4
   Size = 640, Down scale = 2
   • In addition to cutting out the normal
   patches, also cutting out patches shifted by
   a quarter of the patch size.
   Psuedo - Labeled 5 Mask-WSIs
   (Useless for private score !!!)
   Remove
   black / white
   patches for training
   Decoder Block Encoder Block
   15 WSIs
   (Whole Slide Images)
   Predicted 5/10 Mask-WSIs
   for Public/Private
   1 2
   U-Net type model
   Back bone: EfficientNet B3 (ImageNet pretrained)
   3
   4 5
   Resources:
   TITAN RTX, 2080Ti x 2, 1080Ti x 2
   Copyright 2021 Maxwell_110
   Size: 352 x 352
   Down scale: 4
   +
   Size: 640 x 640
   Down scale: 2
   HuBMAP - Hacking the Kidney
   Model Pipeline 17 th place
   6
   1
   Training
   • BCE + Adam(1stLR: 7.5e-4, 2ndLR: 2.5e-4)
   • BS: 8 (size 640), 12 (size 512), 16 (size 352)
   • Patience: ES = 10, RLRoP = 3
   • Training with train images only at first, and
   then with train + public(psuedo) images
   • Alternate training with psuedo labels
   Step 1: Train with Group A, and predict pseudo labels
   for public images
   Step 2: Next, Train with Group B and pseudo labels,
   and predict pseudo labels
   Step 3: Repeat the aboves sufficiently
   Step 4: Submit predictions trained with Group A (+
   pseudo labeld public images)
   3
   2
   Size: 352 x 352
   Down scale: 4
   +
   Size: 512 x 512
   Down scale: 2
   B
   A
   Prediction + Tiling predicted patches
   • TTA: Raw, HorizontalFlip, VerticalFlip
   • Overlapping by 1/16 image size for tiling
   • Naive averaging for ensemble (folds and models)
   • Threshold optimization with train images (th: 0.40)
   Submission on kernel environment
   • Approximately 8 hours running (5 folds, 3 TTA, 2 models)
   • Good correlation between local CV and public LB
   (Alghough there are deviations..., maybe due to the d48 image)
   • Local CV: 0.9429
   Public LB: 0.927
   Private LB: 0.947
   6
   5
   4
   

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