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Aoxiao Zhong Quanzheng Li Team HMS-MGH-CCDS
Harvard Medical School- Massachusetts General Hospital- Center for Clinical Data Science
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Framework Training WSI Tumor probability map Testing WSI patches
predictions Patches with labels fully convolutional networks H * W H/2*W/ H/4*W/4 H/8*W/ H/16*W/16
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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
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Data preprocessing Tissue region segmentation (Otsu’s method of foreground segmentation)
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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
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Data augmentation Random flip Brightness adjustment
Color shift in RGB space Contrast adjustment
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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
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Patch samples Patch Label
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Network architecture Fully convolutional Resnet-101 with dilated convolution and atrous spatial pyramid pooling Feature stride=16 H * W H/16*W/16
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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
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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.
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Result on training set Accuracy of 91% on slide-wise classification
Kappa score of 0.94 on training set
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