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HuBMAP 17th place model pipeline

Maxwell
May 11, 2021

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

Maxwell

May 11, 2021
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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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