Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications
This addresses the need for reliable and effective computer-aided screening in medical imaging, particularly for COVID-19 diagnosis, though it appears incremental as it builds on existing self-supervised learning and loss function methods.
The paper tackled the trade-off between performance and trust in deep learning models for COVID-19 chest X-ray classification by integrating a new surrogate loss with self-supervised learning, resulting in label-efficient networks that are both high-performing and trustworthy.
Effective representation learning is the key in improving model performance for medical image analysis. In training deep learning models, a compromise often must be made between performance and trust, both of which are essential for medical applications. Moreover, models optimized with cross-entropy loss tend to suffer from unwarranted overconfidence in the majority class and over-cautiousness in the minority class. In this work, we integrate a new surrogate loss with self-supervised learning for computer-aided screening of COVID-19 patients using radiography images. In addition, we adopt a new quantification score to measure a model's trustworthiness. Ablation study is conducted for both the performance and the trust on feature learning methods and loss functions. Comparisons show that leveraging the new surrogate loss on self-supervised models can produce label-efficient networks that are both high-performing and trustworthy.