  Breast cancer is one of the most prevalent cancers worldwide and pathologists
are closely involved in establishing a diagnosis. Tools to assist in making a
diagnosis are required to manage the increasing workload. In this context,
artificial intelligence (AI) and deep-learning based tools may be used in daily
pathology practice. However, it is challenging to develop fast and reliable
algorithms that can be trusted by practitioners, whatever the medical center.
We describe a patch-based algorithm that incorporates a convolutional neural
network to detect and locate invasive carcinoma on breast whole-slide images.
The network was trained on a dataset extracted from a reference acquisition
center. We then performed a calibration step based on transfer learning to
maintain the performance when translating on a new target acquisition center by
using a limited amount of additional training data. Performance was evaluated
using classical binary measures (accuracy, recall, precision) for both centers
(referred to as test reference dataset and test target dataset) and at two
levels: patch and slide level. At patch level, accuracy, recall, and precision
of the model on the reference and target test sets were 92.1\% and 96.3\%, 95\%
and 87.8\%, and 73.9\% and 70.6\%, respectively. At slide level, accuracy,
recall, and precision were 97.6\% and 92.0\%, 90.9\% and 100\%, and 100\% and
70.8\% for test sets 1 and 2, respectively. The high performance of the
algorithm at both centers shows that the calibration process is efficient. This
is performed using limited training data from the new target acquisition center
and requires that the model is trained beforehand on a large database from a
reference center. This methodology allows the implementation of AI diagnostic
tools to help in routine pathology practice.
