Multi-pixels Classification for nuclei segmentation in digital pathology based on deep machine learning
Yi Xie · Journal of Physics Conference Series · 2018
The problem this paper solves is identifying the accurate boundary for nuclei in breast cancer digital pathology images. By doing the nuclei segmentation, the output image can be a reference for the doctors to make better diagnosis for breast cancer patients. Using convolutional neural network to solve this problem which is a popular deep learning method for image classification. By generating labelled patches from the original dataset for training and using a sliding window to cut out patches from testing image for classifying pixels in the testing image, the image level classification can be transferred to pixel level classification. A 2x2 receptive field can be classified to 16 classes, and each pixel can be calculated 4 time, which increase the pixel level TPR (true positive rate) from 0.868 to 0.909. And the boundaries are smother than traditional method that the receptive field is 1x1.