  Deep transfer learning using dynamic contrast-enhanced magnetic resonance
imaging (DCE-MRI) has shown strong predictive power in characterization of
breast lesions. However, pretrained convolutional neural networks (CNNs)
require 2D inputs, limiting the ability to exploit the rich 4D (volumetric and
temporal) image information inherent in DCE-MRI that is clinically valuable for
lesion assessment. Training 3D CNNs from scratch, a common method to utilize
high-dimensional information in medical images, is computationally expensive
and is not best suited for moderately sized healthcare datasets. Therefore, we
propose a novel approach using transfer learning that incorporates the 4D
information from DCE-MRI, where volumetric information is collapsed at feature
level by max pooling along the projection perpendicular to the transverse
slices and the temporal information is contained either in second-post contrast
subtraction images. Our methodology yielded an area under the receiver
operating characteristic curve of 0.89+/-0.01 on a dataset of 1161 breast
lesions, significantly outperforming a previous approach that incorporates the
4D information in DCE-MRI by the use of maximum intensity projection (MIP)
images.
