Machine Learning Reduces Uncertainty in Breast Cancer Diagnoses

A Michigan Tech-developed machine learning model uses probability to more accurately classify breast cancer shown in histopathology images and evaluate the uncertainty of its predictions. Breast cancer is the most common cancer with the highest mortality rate. Swift detection and diagnosis diminish the impact of the disease. However, classifying breast cancer using histopathology images -- tissues and cells examined under a microscope -- is a challenging task because of bias in the data and the unavailability of annotated data in large quantities. Automatic detection of breast cancer using convolutional neural network (CNN), a machine learning technique, has shown promise -- but it is associated with a high risk of false positives and false negatives.