AMDet: A Tool for Mitotic Cell Detection in Histopathology Slides

TL;DR

This paper evaluates the effectiveness of Microsoft's AutoML tool in automatically detecting mitotic cells in breast cancer histopathology slides, aiming to improve diagnostic efficiency and accuracy.

Contribution

It provides the first formal assessment of AutoML for mitotic cell detection in histopathology images, demonstrating its potential utility.

Findings

AutoML shows promising accuracy in mitotic cell detection

The tool reduces manual workload for pathologists

AutoML's performance varies with different training configurations

Abstract

Breast Cancer is the most prevalent cancer in the world. The World Health Organization reports that the disease still affects a significant portion of the developing world citing increased mortality rates in the majority of low to middle income countries. The most popular protocol pathologists use for diagnosing breast cancer is the Nottingham grading system which grades the proliferation of tumors based on 3 major criteria, the most important of them being mitotic cell count. The way in which pathologists evaluate mitotic cell count is to subjectively and qualitatively analyze cells present in stained slides of tissue and make a decision on its mitotic state i.e. is it mitotic or not? This process is extremely inefficient and tiring for pathologists and so an efficient, accurate, and fully automated tool to aid with the diagnosis is extremely desirable. Fortunately, creating such a tool is made significantly easier with the AutoML tool available from Microsoft Azure, however to the best of our knowledge the AutoML tool has never been formally evaluated for use in mitotic cell detection in histopathology images. This paper serves as an evaluation of the AutoML tool for this purpose and will provide a first look on how the tool handles this challenging problem. All code is available athttps://github.com/WaltAFWilliams/AMDet