Team HMS-MGH-CCDS
Aoxiao Zhong
Quanzheng Li
Harvard Medical SchoolMassachusetts General HospitalCenter for Clinical Data Science
Training WSI
Framework
Tumor probability map
Testing WSI
patches
predictions
Patches with labels
fully convolutional networks
H * W H/2*W/2
H/4*W/4
H/8*W/8
H/16*W/16
Data selection
• All Camelyon16 training/testing slides
(exclude slides in which tumors are not
annotated exhaustively )
• Positive slides with annotations in camelyon17
training set
• All Negative slides in camelyon17 training set
Data preprocessing
• Tissue region segmentation (Otsu’s method of
foreground segmentation)
Patch extraction
• At 20x
• 960 x 960 for training(due to memory
limitation of GPU)
• Extracted randomly on-the-fly
• Equal probability of with or without positive
region in the patch
Data augmentation
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Random flip
Brightness adjustment
Color shift in RGB space
Contrast adjustment
Hard example mining
• Weighted mask for hard example mining
• Got from a whole round of inference on all the
training slides
• Weight = probability of being classified
incorrectly
• False positives have a higher probability of
been chosen as training patches
• Only done once. Multiple rounds show no
significant improvement
Patch samples
Patch
Label
Network architecture
• Fully convolutional Resnet-101 with dilated
convolution and atrous spatial pyramid
pooling
• Feature stride=16
H*W
H/16*W/16
Network training
• Model is trained with a Microsoft-coco pretrained
model using mini-batch SGD
• Trained on Nvidia DGX-1:
8 x Nvidia Tesla P100
• 10000 iterations without hard example mining
• 10000 iterations with hard example mining
• 28 hours in total
Post-processing
Random forest classifier on heatmap-based features given for slide
classification
Features include:
1.Max value of the heatmap
2. Area of largest connected region
3. Major axis length of largest connected region
4. Area predicted as tumor in total
5. …
…
pN-stage is determined based on slide-wise prediction with the rules
given.
Result on training set
• Accuracy of 91% on slide-wise classification
• Kappa score of 0.94 on training set