  Objective: In this work, we perform margin assessment of human breast tissue
from optical coherence tomography (OCT) images using deep neural networks
(DNNs). This work simulates an intraoperative setting for breast cancer
lumpectomy. Methods: To train the DNNs, we use both the state-of-the-art
methods (Weight Decay and DropOut) and a newly introduced regularization method
based on function norms. Commonly used methods can fail when only a small
database is available. The use of a function norm introduces a direct control
over the complexity of the function with the aim of diminishing the risk of
overfitting. Results: As neither the code nor the data of previous results are
publicly available, the obtained results are compared with reported results in
the literature for a conservative comparison. Moreover, our method is applied
to locally collected data on several data configurations. The reported results
are the average over the different trials. Conclusion: The experimental results
show that the use of DNNs yields significantly better results than other
techniques when evaluated in terms of sensitivity, specificity, F1 score,
G-mean and Matthews correlation coefficient. Function norm regularization
yielded higher and more robust results than competing methods. Significance: We
have demonstrated a system that shows high promise for (partially) automated
margin assessment of human breast tissue, Equal error rate (EER) is reduced
from approximately 12\% (the lowest reported in the literature) to 5\%\,--\,a
58\% reduction. The method is computationally feasible for intraoperative
application (less than 2 seconds per image).
