AI reduces false positives in screening mammography

Following the assumption that there may be nuanced features associated with some mammogram images that could lead to an unnecessary recall when interpreted by a radiologist, the researchers used a method based on convolutional neural networks (CNNs) to build a computer toolkit that could identify those images. The researchers trained CNN models using 14,860 images of 3,715 patients from the Full-Field Digital Mammography Dataset and the Digital Dataset of Screening Mammography. They investigated six classification scenarios that would help distinguish images of benign, malignant, and recalled-benign mammograms.