  Building AI models with trustworthiness is important especially in regulated
areas such as healthcare. In tackling COVID-19, previous work uses
convolutional neural networks as the backbone architecture, which has shown to
be prone to over-caution and overconfidence in making decisions, rendering them
less trustworthy -- a crucial flaw in the context of medical imaging. In this
study, we propose a feature learning approach using Vision Transformers, which
use an attention-based mechanism, and examine the representation learning
capability of Transformers as a new backbone architecture for medical imaging.
Through the task of classifying COVID-19 chest radiographs, we investigate into
whether generalization capabilities benefit solely from Vision Transformers'
architectural advances. Quantitative and qualitative evaluations are conducted
on the trustworthiness of the models, through the use of "trust score"
computation and a visual explainability technique. We conclude that the
attention-based feature learning approach is promising in building trustworthy
deep learning models for healthcare.
