  In this extended abstract, we will present and discuss opportunities and
challenges brought about by a new deep learning method by AUC maximization (aka
\underline{\bf D}eep \underline{\bf A}UC \underline{\bf M}aximization or {\bf
DAM}) for medical image classification. Since AUC (aka area under ROC curve) is
a standard performance measure for medical image classification, hence directly
optimizing AUC could achieve a better performance for learning a deep neural
network than minimizing a traditional loss function (e.g., cross-entropy loss).
Recently, there emerges a trend of using deep AUC maximization for large-scale
medical image classification. In this paper, we will discuss these recent
results by highlighting (i) the advancements brought by stochastic non-convex
optimization algorithms for DAM; (ii) the promising results on various medical
image classification problems. Then, we will discuss challenges and
opportunities of DAM for medical image classification from three perspectives,
feature learning, large-scale optimization, and learning trustworthy AI models.
