Slide 22
Slide 22 text
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
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
(Unfortunately PL was 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)
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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
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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)
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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)
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5
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