Transfer learning for the generalization of artificial intelligence in breast cancer detection: a case-control study.

Background: Some researchers have questioned whether artificial intelligence (AI) systems maintain their performance when used for women from populations not considered during the development of the system.

Purpose: To evaluate the impact of transfer learning as a way of improving the generalization of AI systems in the detection of breast cancer.

Material And Methods: This retrospective case-control Finnish study involved 191 women diagnosed with breast cancer and 191 matched healthy controls.

A Comparison of Regions of Interest in Parenchymal Analysis for Breast Cancer Risk Assessment.

Computerized parenchymal analysis has shown potential to be utilized as an imaging biomarker to estimate the risk of breast cancer. Parenchymal analysis of digital mammograms is based on the extraction of computerized measures to build machine learning-based models for the prediction of breast cancer risk. However, the choice of the region of interest (ROI) for feature extraction within the breast remains an open problem.

A New Benchmark and Method for the Evaluation of Chest Wall Detection in Digital Mammography.

CAD systems have shown good potential for improving breast cancer diagnosis and anomaly detection in mammograms. A basic enabling step for the utilization of CAD systems in mammographic analysis is the correct identification of the breast region. Therefore, several methods to segment the pectoral muscle in the medio-lateral oblique (MLO) mammographic view have been proposed in the literature.