Abstract
When analysing screening mammograms, radiologists can naturally process
information across two ipsilateral views of each breast, namely the
cranio-caudal (CC) and mediolateral-oblique (MLO) views. These multiple related
images provide complementary diagnostic information and can improve the
radiologist's classification accuracy. Unfortunately, most existing deep
learning systems, trained with globally-labelled images, lack the ability to
jointly analyse and integrate global and local information from these multiple
views. By ignoring the potentially valuable information present in multiple
images of a screening episode, one limits the potential accuracy of these
systems. Here, we propose a new multi-view global-local analysis method that
mimics the radiologist's reading procedure, based on a global consistency
learning and local co-occurrence learning of ipsilateral views in mammograms.
Extensive experiments show that our model outperforms competing methods, in
terms of classification accuracy and generalisation, on a large-scale private
dataset and two publicly available datasets, where models are exclusively
trained and tested with global labels.