Fast WSI Prediction

Fast WSI prediction by ScanNet in PyTorch.

Aim: undergraduate course

Invasive Ductal Carcinoma (IDC) detection on Whole Slide Image (WSI) by deep learning method equipped with ScanNet fully conv. scheme for fast WSI prediction.

Data

279 slides with IDC ROI annotations.
Ratio of 6:2:2 for training, validation and testing separation.

Tiling small patch with 50*50 at 2.5x magnification for non-IDC and IDC class, binary classification task.

patches distribution	non-IDC	IDC
training	111090	49204
validation	45356	15354
testing	42292	14228

Method

1. Replace the original VGG with VGG_fullyConv for training
2. Train the VGG_fullyConv model for classification, same as original training process
3. Check the performance on Validation dataset and select the best model in terms of F1 score
4. Test and evaluate your trained VGG_fullyConv model on Testing dataset in patch-level
5. Input the block with bigger size, Infer and predict the probability map for one WSI and show it!

HotSpot

We significantly improve the inferring time by the ScanNet scheme.

Implementation

Replaced the last GAP and fc. in VGG with AvgPooling with 2x2 kernel size followed by 2 convs with 1x1 kernel, s6_predWSI.py

self.avgpool = nn.AvgPool2d(kernel_size=2, stride=2)

self.classifier = nn.Sequential(
    nn.Conv2d(out_channel, out_channel, kernel_size=1, stride=1),
    nn.BatchNorm2d(out_channel),
    nn.ReLU(inplace=True),
    nn.Conv2d(out_channel, num_classes, kernel_size=1, stride=1))

Also, we modify the VGG network due to our small training size, and, we remove padding operation in convolutional layer to avoid the border effect arcoss testing blocks. You can refer to myModelVgg.py for more details.
The overall network structure for our architecture is shown:

Our overall network structure

Noting: The core part for Fast WSI is the step of sliding window of testing block. You can refer to s6_predWSI.py for details in function fast_wsi_pred()

Result

When training

We shows the curves of training loss and validation accuracy, see below:

Training loss curve	Validation accuracy

Independent testing evaluation (patch-level)

Confusion matrics	Postive pred	Negtive pred
Postive	11041	3187	= 14228
Negtive	3391	38901	= 42292

• Accuracy : 0.8836
• Specificity : 0.9198
• Recall : 0.7760
• Precision : 0.7650
• F1Score : 0.7705

WSI probability map prediction (slide-level)

The valuable thing is that we achieve a fast WSI prediction method, not accuracy or performance improvement.
We show one WSI prediction probability map below, the size of the original WSI is 13440*10560, and the size of the output prediction map is 834*654.
it only takes about 4s!

Original WSI Image	Predicted Prob. Maps