Direct Cellularity Estimation on Breast Cancer Histopathology Images Using Transfer Learning

Figure 3

Overview workflow of our methods. The images are first preprocessed to obtain consistent stain. Thousands of features are extracted from each image using the pretrained CNNs and are pooled to enhance rotation invariance. A few hundred features are obtained from mRMR feature selection and PCA and are used to train GBDT and SVM.