Risks of feature leakage and sample size dependencies in deep feature extraction for breast mass classification.
Journal:
Medical physics
Published Date:
Apr 12, 2021
Abstract
PURPOSE: Transfer learning is commonly used in deep learning for medical imaging to alleviate the problem of limited available data. In this work, we studied the risk of feature leakage and its dependence on sample size when using pretrained deep convolutional neural network (DCNN) as feature extractor for classification breast masses in mammography.