Fast tumor detector in whole-slide image with dynamic programing based Monte Carlo sampling

In the last decade, computational pathology has attracted notable attention in the deep learning domain. However, even on the state-of-the-art deep learning computing platforms, a high-resolution scanned whole slide image (WSI) still requires reducing into massive patches to be processed, which is very time consuming in real-time diagnosis. In this paper, we propose a high-throughput tumor location system with Monte Carlo adaptive sampling to accelerate WSI analysis. Additionally, we design a dynamic programming framework to incorporate spatial correlation, which can iteratively eliminate false positives or false negatives in the identification or tumor tissues. We use three datasets of colorectal cancer from The Cancer Genome Atlas (TCGA) for performance evaluation. The designed computer-aided system can reduce more than 50% of the diagnostic time on average in the tumor location task, along with a slight increase in accuracy.