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Objective: In this work, we perform margin assessment of human breast tissuefrom optical coherence tomography (OCT) images using deep neural networks(DNNs). This work simulates an intraoperative setting for breast cancerlumpectomy. Methods: To train the DNNs, we use both the state-of-the-artmethods (Weight Decay and DropOut) and a newly introduced regularization methodbased on function norms. Commonly used methods can fail when only a smalldatabase is available. The use of a function norm introduces a direct controlover the complexity of the function with the aim of diminishing the risk ofoverfitting. Results: As neither the code nor the data of previous results arepublicly available, the obtained results are compared with reported results inthe literature for a conservative comparison. Moreover, our method is appliedto locally collected data on several data configurations. The reported resultsare the average over the different trials. Conclusion: The experimental resultsshow that the use of DNNs yields significantly better results than othertechniques when evaluated in terms of sensitivity, specificity, F1 score,G-mean and Matthews correlation coefficient. Function norm regularizationyielded higher and more robust results than competing methods. Significance: Wehave demonstrated a system that shows high promise for (partially) automatedmargin assessment of human breast tissue, Equal error rate (EER) is reducedfrom approximately 12\% (the lowest reported in the literature) to 5\%\,--\,a58\% reduction. The method is computationally feasible for intraoperativeapplication (less than 2 seconds per image).

Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks