Robust Medical Image Analysis using Privileged Information

Abstract

Abstract Domain adaptation is a crucial task in medical diagnosis and treatment planning, as it enables models trained on large, labeled datasets to be effectively applied to smaller, domain-specific datasets. This is particularly challenging due to data scarcity and shifts in data distribution. Privileged Information (PI), such as binary attributes or bounding boxes, has the potential to improve machine learning models’ adaptability across diverse domains. This study aims to investigate the role of PI in domain adaptation for medical image classification. The results of this experiment indicate that integrating PI led to increased accuracy and stabilized prediction accuracies. Furthermore, the findings affirm the importance of both the quantity and correlation of the PI provided and its correlation with output labels in enhancing model performance, thereby supporting the fundamental principles of domain adaptation. Moreover, the study underscores the significance of strategically considering PI attributes during model training to achieve stable output accuracy and effectively mitigate domain shift. This comprehensive study will help improve diagnostic accuracy in various domains, especially healthcare, which can lead to more effective treatments and better patient outcomes.