A Fast Pyramidal Bayesian Model for Mitosis Detection in Whole-Slide Images

Mitosis detection in Hematoxylin and Eosin images and its quantification for mm\(^2\) is currently one of the most valuable prognostic indicators for some types of cancer and specifically for the breast cancer. In whole-slide images the main goal is to detect its presence on the full image. This paper makes several contributions to the mitosis detection task in whole-slide in order to improve the current state of the art and efficiency. A new coarse to fine pyramidal model to detect mitosis is proposed. On each pyramid level a Bayesian convolutional neural network is trained to compute class prediction and uncertainty on each pixel. This information is propagated top-down on the pyramid as a constraining mechanism from the above layers. To cope with local tissue and cell shape deformations geometric invariance is also introduced as a part of the model. The model achieves an F1-score of 82.6% on the MITOS ICPR-2012 test dataset when trained with samples from skin tissue. This is competitive with the current state of the art. In average a whole-slide is analyzed in less than 20 s. A new dataset of 8236 mitoses from skin tissue has been created to train our models.