Learning using privileged information for segmenting tumors on digital mammograms
This work addresses data scarcity and privacy issues in medical imaging for healthcare applications, but it is incremental as it adapts an existing technique to a specific domain.
The paper tackled the problem of limited and restricted medical data by applying Learning Using Privileged Information to improve tumor segmentation on digital mammograms, achieving a 10% higher F1 score compared to the baseline.
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.