  Traditionally, deep learning methods for breast cancer classification perform
a single-view analysis. However, radiologists simultaneously analyze all four
views that compose a mammography exam, owing to the correlations contained in
mammography views, which present crucial information for identifying tumors. In
light of this, some studies have started to propose multi-view methods.
Nevertheless, in such existing architectures, mammogram views are processed as
independent images by separate convolutional branches, thus losing correlations
among them. To overcome such limitations, in this paper, we propose a
methodological approach for multi-view breast cancer classification based on
parameterized hypercomplex neural networks. Thanks to hypercomplex algebra
properties, our networks are able to model, and thus leverage, existing
correlations between the different views that comprise a mammogram, thus
mimicking the reading process performed by clinicians. This happens because
hypercomplex networks capture both global properties, as standard neural
models, as well as local relations, i.e., inter-view correlations, which
real-valued networks fail at modeling. We define architectures designed to
process two-view exams, namely PHResNets, and four-view exams, i.e., PHYSEnet
and PHYBOnet. Through an extensive experimental evaluation conducted with
publicly available datasets, we demonstrate that our proposed models clearly
outperform real-valued counterparts and state-of-the-art methods, proving that
breast cancer classification benefits from the proposed multi-view
architectures. We also assess the method generalizability beyond mammogram
analysis by considering different benchmarks, as well as a finer-scaled task
such as segmentation. Full code and pretrained models for complete
reproducibility of our experiments are freely available at
https://github.com/ispamm/PHBreast.
