Deep learning performance for detection and classification of microcalcifications on mammography.

ABSTRACT:

Background

Breast cancer screening through mammography is crucial for early detection, yet the demand for mammography services surpasses the capacity of radiologists. Artificial intelligence (AI) can assist in evaluating microcalcifications on mammography. We developed and tested an AI model for localizing and characterizing microcalcifications.

Methods

Three expert radiologists annotated a dataset of mammograms using histology-based ground truth. The dataset was partitioned for training, validation, and testing. Three neural networks (AlexNet, ResNet18, and ResNet34) were trained and evaluated using specific metrics including receiver operating characteristics area under the curve (AUC), sensitivity, and specificity. The reported metrics were computed on the test set (10% o

SUBMITTER: Pesapane F 

PROVIDER: S-EPMC10630180 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature