Deep learning out-performs vet. pathologists in finding the mitotically most active tumor region
Channel: Marc Aubreville
Date: 2020-10-05

This is a short announcement for our recently published Scientific Reports paper. For the assessment of how malignant a tumor is, that is how likely it spreads to surrounding tissue or the complete organism, we take a thin slice of tissue and assess it under the microscope. Depending on the tumor type we consider different criteria to determine the malignancy. One of these is the question: How often are cells in the tissue dividing? The rate of this process, denoted mitosis, is known to be one of the most predictive factors for the spread of the tumor.However, since under the microscope we can't see how tissue develops over time, we can only assess how often we see mitosis.
It is believed that the area of the tumor with the highest rate is reflecting the malignancy of the tumor. The magnification required to identify mitoses is really high. But we need to know the density over the complete tissue.We believed that this is truly challenging for a human. But we wanted to find out, how challenging. For this, however, we need to know each of those mitotic figures on the microscopy slide. This is something that's typically not available.

Luckily, we just created a really big open dataset of a highly relevant dog tumor, for which our pathologists spent months worth of annotation work. 

This dataset provides us for the first time with complete maps of verified mitotic figures on microscopy images.We wanted to know how well expert pathologists would score in identifying the region where most mitotic activity can be found. And, since we already saw promising results in automatic identification of those cells, we also wanted to know how well different deep learning based computerized approaches would perform on this task. If we now caught your interest in what we did, and what came out of that, you can read all about our research at our recently published Scientific Reports paper. And just as a small spoiler: Finding the mitotically most active region was indeed a really hard task for humans

Paper link: https://www.nature.com/articles/s41598-020-73246-2