  The implementation of deep learning based computer aided diagnosis systems
for the classification of mammogram images can help in improving the accuracy,
reliability, and cost of diagnosing patients. However, training a deep learning
model requires a considerable amount of labeled images, which can be expensive
to obtain as time and effort from clinical practitioners is required. A number
of publicly available datasets have been built with data from different
hospitals and clinics. However, using models trained on these datasets for
later work on images sampled from a different hospital or clinic might result
in lower performance. This is due to the distribution mismatch of the datasets,
which include different patient populations and image acquisition protocols.
The scarcity of labeled data can also bring a challenge towards the application
of transfer learning with models trained using these source datasets. In this
work, a real world scenario is evaluated where a novel target dataset sampled
from a private Costa Rican clinic is used, with few labels and heavily
imbalanced data. The use of two popular and publicly available datasets
(INbreast and CBIS-DDSM) as source data, to train and test the models on the
novel target dataset, is evaluated. The use of the semi-supervised deep
learning approach known as MixMatch, to leverage the usage of unlabeled data
from the target dataset, is proposed and evaluated. In the tests, the
performance of models is extensively measured, using different metrics to
assess the performance of a classifier under heavy data imbalance conditions.
It is shown that the use of semi-supervised deep learning combined with
fine-tuning can provide a meaningful advantage when using scarce labeled
observations. We make available the novel dataset for the benefit of the
community.
