A Comparison of Techniques for Class Imbalance in Deep Learning Classification of Breast Cancer.

ABSTRACT: Tools based on deep learning models have been created in recent years to aid radiologists in the diagnosis of breast cancer from mammograms. However, the datasets used to train these models may suffer from class imbalance, i.e., there are often fewer malignant samples than benign or healthy cases, which can bias the model towards the healthy class. In this study, we systematically evaluate several popular techniques to deal with this class imbalance, namely, class weighting, over-sampling, and under-sampling, as well as a synthetic lesion generation approach to increase the number of malignant samples. These techniques are applied when training on three diverse Full-Field Digital Mammography datasets, and tested on in-distribution and out-of-distribution samples. The experiments show that

SUBMITTER: Walsh R 

PROVIDER: S-EPMC9818528 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature