Figure A1.

A depiction of the two-stage mitosis detection approach. On the top, in stage 1, 20× magnification images and annotations from the updated refined mitoses dataset are used to train a Faster R-CNN model (the model is also presented in Figure 1). Optimal probability thresholds are applied on the output candidates, which are determined from the validation set (based on Equation (4)). These selected candidates are then extracted (size 64 × 64 pixels) at 40× magnification from the original Whole Slide Images (WSIs) and passed into the second stage. On the bottom shows stage 2 where the extracted patches are fed into a DenseNet-161 ImageNet pre-trained feature extractor, where the outputs are fed into a logistic regression classifier to determine whether the candidates are mitosis or difficult false positives.