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.

翻译：由于GDPR合规要求导致的数据有限性和共享限制，构成了医学数据可用性和可获取性降低的两个常见因素。为解决这些问题，我们引入了利用特权信息学习技术。为验证这一思路，我们尝试构建一个鲁棒模型，通过训练过程中获取特权信息知识来提升数字乳腺X线图像中肿瘤的分割质量。为此，基线模型（称为学生模型）从原始乳腺X线影像提取的补丁上进行训练，而具有相同架构的辅助模型（称为教师模型）则通过访问相应增强补丁来获取特权信息。我们通过本次引入教师模型辅助的方式重复学生模型训练流程。实验结果表明，所提出的方法在多数情况下表现更优，与基线相比F1分数可提升10%。