@article{XI_He_Ebadi_Tremblay_Wong_2022, title={Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications}, volume={7}, url={https://openjournals.uwaterloo.ca/index.php/vsl/article/view/4906}, DOI={10.15353/jcvis.v7i1.4906}, abstractNote={<p>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.</p>}, number={1}, journal={Journal of Computational Vision and Imaging Systems}, author={XI, PENGCHENG and He, Siyuan and Ebadi, Ashkan and Tremblay, Stéphane and Wong, Alexander}, year={2022}, month={Apr.}, pages={37–39} }