Learning using privileged information for segmenting tumors on digital
  mammograms
Ioannis N. Tzortzis Konstantinos Makantasis Ioannis Rallis Nikolaos Bakalos Anastasios Doulamis Nikolaos Doulamis
Limited amount of data and data sharing restrictions, due to GDPR compliance,
constitute two common factors leading to reduced availability and accessibility
when referring to medical data. To tackle these issues, we introduce the
technique of Learning Using Privileged Information. Aiming to substantiate the
idea, we attempt to build a robust model that improves the segmentation quality
of tumors on digital mammograms, by gaining privileged information knowledge
during the training procedure. Towards this direction, a baseline model, called
student, is trained on patches extracted from the original mammograms, while an
auxiliary model with the same architecture, called teacher, is trained on the
corresponding enhanced patches accessing, in this way, privileged information.
We repeat the student training procedure by providing the assistance of the
teacher model this time. According to the experimental results, it seems that
the proposed methodology performs better in the most of the cases and it can
achieve 10% higher F1 score in comparison with the baseline.