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Effective representation learning is the key in improving model performancefor medical image analysis. In training deep learning models, a compromiseoften must be made between performance and trust, both of which are essentialfor medical applications. Moreover, models optimized with cross-entropy losstend to suffer from unwarranted overconfidence in the majority class andover-cautiousness in the minority class. In this work, we integrate a newsurrogate loss with self-supervised learning for computer-aided screening ofCOVID-19 patients using radiography images. In addition, we adopt a newquantification score to measure a model's trustworthiness. Ablation study isconducted for both the performance and the trust on feature learning methodsand loss functions. Comparisons show that leveraging the new surrogate loss onself-supervised models can produce label-efficient networks that are bothhigh-performing and trustworthy.

Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications